{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) 练习\n",
    "\n",
    "\n",
    "k-NN分类器由两个阶段组成\n",
    "\n",
    "- 在训练过程中，分类器接受训练数据，并记录下来。\n",
    "- 在测试阶段，kNN通过与所有训练图像进行比较并记录下k个最相似训练样本的标签，通过判断标签来对每个测试图像进行分类。\n",
    "- k值需要交叉验证来确认。\n",
    "\n",
    "在这个练习中，你需要实现这些不同的阶段所需的程序，同时理解图像分类的基本流程，交叉验证法，编写效率更高的向量化的代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在notebook运行这些启动程序.（这部分的代码属于启动代码，不需要理会，只要可以正常运行就可以）\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from DSVC.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "# 在notebook中嵌套现实一些图像。不用理会这部分的代码。\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15., 12.) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (20000, 32, 32, 3)\n",
      "Training labels shape:  (20000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# 导入原始的CIFAR-10数据\n",
    "cifar10_dir = 'DSVC/datasets/cifar-10-batches-py' \n",
    "# 你需要将CIFAR-10的数据放在这个路径下。\n",
    "\n",
    "\n",
    "# 为了避免一些内存的问题，只导入了30000张图片的数据，参数3表示batch的组数。\n",
    "# 你也可以将3改为6，去导入数据集的全部数据（60000张图片的数据）\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir, 3)\n",
    "\n",
    "# 输出训练数据跟测试数据的维度.\n",
    "print ('Training data shape: ', X_train.shape)\n",
    "print ('Training labels shape: ', y_train.shape)\n",
    "print ('Test data shape: ', X_test.shape)\n",
    "print ('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a0quxAta6KOXhugprBVSAJeW34OKoLNZaRLkEmQomN4ZEVZL2MjaKUb6P8jKI8tBGQFKljh0tbkkyIAy76CTD2ESO8ckcxUKRMEooVQpUpspsNjs01tZpNrt4vkBPE8UJY2OT1Nc2qNU2mBnPsbS8QLXaYmyqghQquK4wCGOcbI7FanUkHVonJEmamXfmHNBZY8NaHMfh/vsf4NDjj3H7a2/jygNXkxiFFRA0YjSB5/AXf/bnPPnVB3jX296B8gOsSRCer8hHwXUUjqNSIwVD6EVENibpaXZPX8k9X3qaSjZg0IxwPQUC2ZxPLpvFRBYjoMSjP0hwnYQg8LBWUMrFYpienqGQz9HJjnbbJwNNkPEI3AzWekyUJymVShhjiKIuy2uLRL2Ew4ee5Xf/5/9gfHKK99z5VpS1uMJWVYceGmeDJGHh9CnGimO0uhGbjRadQchbX/MdFx4Xmx74tGIJnBgxQrddZ3X1BNkMPHv0EDru0u81kXKPZq3JxsopikGR1Y1lCkUXq7vUa5vk/Skq4xGtzirWdNFRA4cqjzz4V/QH3ZH8yPguIhbHERxS3XVGRymrsGLQKl2wnNASdwesLhyntrTE2tGjnF5coqeEA698BW/9vu8eGq6CMRZDKkdWFGIuSYensnLJd74EMDq1WIRU6CwWQc4uZwY7XM2GlreVoRUOpDo7/bM2rT9kHljEWuSsyIyeJKXSPJubdTKZmHa1Sb+tuOexZ2jgYYMAx/VZXOugI59ACb1am7XlVa648gr0rnnCgsd4zmPX3Dh798+zvFLj1InTrJ1aoBtZdu/aRTLoQLM1ko4otBib4BoNTgA2wY03mXU70G7RKOxDeznEJmitQDTWpladY0C0BWNRaJRyEAXWAJFGlE35DEMlfWE4jocxCcYm6MEAJT7KCVCOwnccFHFqPQ2VmSgXi6DEQXkVkmAMR3ooY+n1VmFgcbwAL1MAlcE6GcS4CP5IOjI5j9niJIVsEdf12L17Dq0tJ46v0O/HtPt9stkMa2s1tIZcwaFWi+kPIo4dO0WlUmF+9y4c7XHtzbM89eQJosih2egxOz/NfXd/jSBfZM/+a0bSceagqpxnEyMiuK7LwuJpPvbbv8nCfV/ijne+i9e+67sBH2UF31WsLy3wiT/8CAf3HEBHPXArSDzcbVp73mefCwU4ykFLiJ/1sBlodJvsntlJayNBDxROAL5KlUcUG6byAZPjY1SXmwTZLHFsqdW6WANB0KdSKeIah3whj+t49Ht9wsHo1PZoEBH3QnzH5dZXv44gk6HX7dLr9RkvTWCtwc0ETM1MMz47wcrGMqJS2UwTdkAN+xtZA6Kod5p8/FOf5tZbXskgMvTDBHNRT69FxKAcENOhXttk4eRRfD8iidqsrZ7EdRNcR9NpbTBRCZidKtJtV/EE0DGewPpmlTAveNVTaBPTH2yS8RJymQ6txlHC6Hyn+bdQYTRxFGP8DBoXAYy1MFxwdb9Pv92m12jSbQ3YWFnl0S9+lvXTC7SaDbwgIF8eY2Vxkbe8/92pkSSCWEFjiK1BG8AMDdxLwOVV5EhqEYhKB81KajUComT4fQgR1NACAoiNPbv1sKRbFuDstm24jxkq+9GK608/+kkmJ1yuPrCHZ59xaHSeJfEKmMEAbTUmEzOJxgQt+ps1JirTvPrO7yPqJui8h8TjTOc8OlGPnWqGfRO7Ke06yPj6BscWFpidLtOsPkuxOnplV1ZjrMUYhZtxyBpQKk+t1cAefwi9r4zkM4ShEKvU6RTpGBHBc12U42KNBquBCBHBcRwcV6GUxfMsxpiLLmzhoE/Yb2GJ6a0s0Whu0uk0KZVLFEolMrkAz89g3RyOG+B6PkoFuG4Rzyni5+YZJBrPFbKZFkZ3MSYmjjuIq/GDAtYEWBOMpAMrdFt9NldDjB4j7C2wc6ZCa03z9KFl9u6ZYteBKfaUJqlmuywt1di9ZzcHb7yWV9x2A+ViDt9THHn6OD//v/8xXiDkCj77rryKQw8d4Y237SY0Ci9oj6ZjC5wh64TnppSNE974hjfwzN2fxh59ipWvfpL1m64k5+eJk4R2a4NedYEdFZjfMYbrWaxOsCZV5MPRv+jOUUcJYgRtNbFVFCt5skmOhQdXWFmNqRQUZd9isxk2OwNc31CqFOknXUKJIOuA67L7inmCjE99s02j1caNBVEOjUaLbruFr0a/9fgtd9zJkWeewRWPmZkxfDdL24Nm7TTPnKhTKI+xf3Yvb3vT2zhw9RybrVX+6Iu/T2xDhJhw0KPX79FsNmm3V1COixcE6MDwuUe/wiAaYDQ4yuXv3HXhw9if/sSvc/CafdQ21zh57Amam00qpRxxVCcb+HS6pyjkA7JBwiBq0Q5dOmGDTqtFqXQFzx49Sm1zlfGxSVrhJrX6Q+i4h++HZIMMzx57nGJ5AmNG8+Puz36RA1fMM1muMD8/j+97WAtaa3CET370o6w8+gR5hKWwj1Mp89DDj/H0s0eohgNcNFdNTnLD9AyHn36Gg9fdkOovEaqtHl4+g9FndtKXhsuuyCH1awuCI6DEIqQrtmEo6MPV21pw1HA7qwRtLdpYtE0XBGstGp5T2yJppYswwOoWvabH4rFVNnt9tPFIvBxLq3V0rFAZjzge4CK8801v4va3vJ7qoEOr2qEZd9kxWWI2W6AvHr3I0otDWonGBBl62jCze4bFgkfeGz1TlXJAa5TjoEQQFNoIUphj/OrXsBYJNtFYC4kk6Yo/XNy01jiOQ+p7MlhiXNdBRKEMiKMQZXHO4+P9On4kfUzYJ+53WVs4TrPV5OjRZynkAyYnx5icmyebK+I4WbqDkEEY42YKVMrjFAp58vkM2fxuTNIBpVBeDmsTHG1Q1qDEwSgHbUdbF71OSOAFaEmY2DHJZJBhqhdy3fVXESaWguczqzIEbY+5nXMcpk2hUGRuukTOT+j2auTHJ8kFwnXXzaBUwJFnlnjqgUM4EvJI0qJcKePkmxflyVmLWclQJlOrSyEYY6iUK9zy6ldwrHuaYiVH1lOUJybBccnmM/Qzire88w7uf2QFq7KI8UgPUw53lGd2mqP40eqhcl7q5hEPlwzHnz5Nv9qj14a5KzM4SUwcWRzP4PoCSiFArpwjjhx0lOAFDsYY5nfOktkMGEQD4jgmDEOUOHTag5F0NDtLdAcbHNx/EwNtyARFGo0TlAoFTvbXqEz4iBUeeuBBnnkmIlNKUNN98BN0mGCtZTAYEA5CSn4Zaww60sRxgvgu2YKHTZKzEbEL4bGH/4oTxwLipMvc3CxaNwkCiMIacZIhn3PI5jwazQ12zl2D+JpWv4tVBdphk2a/TlB0URkolMc5eew4e3ZM4joOrVbI+MQcsRZq9dEG2K/9yv/NztkKY6UK1113kLHxMYqFIsViiemZSQ7ccC2+C3nPoRjF1Pshb9i1jytOLbG0sMaxw0/QbKyyuLFKt93GJAniOogVkigmyGVQqeuBb0uL3HPS7YMdWt6OSv+3GJQYHMc9a/nYoaAb1HDbIsOtJkM3i6ANRMZirE39k3ZoezqjO1/Ml/DEpVZtsNJsIuLT7obUO120ctBhQj7jccP1V3H19QdohgkTUznKUw7ZzT6ToskpA65itRPSiTStQUxkHZwk4q5br6LxyCTBYHMkHVonGGuJ4zi1zBMQDKEV8qqIWEOiGVrcoJQ8z7Y2xuDggCjAYLSQCFirSRJLGFqsObONv7CVoXVCEke0Gg0WTp4k0ZpKqUK5VKDX6XDy2GkQh8FAs7y6TrPdIbbC+Pg4uWyWK/fv57WvvZVyeYJ+pFGicdCIazBxiEo0KFDuaNdKLlBMzeTpdQd4WYcdxQK76y6zuRL7rx5jxmbZX5hksb1GUE54+1uv55mnD7Fw7FFMskGQzbP4zJO0m5uU8zFx1OPKPQG+B606VNcNpxfWcLKdkXRslR6zJdYkwwAW1uJ5LgdvuZnaiYfZc93VzO/YhfXGSICS5+CphH0H+1T1FPnKOEbrNK7D0HLj/K6brTi9sEF2LIs3Y7CBz+LTVepLPbwYSnnBzznofgxWE4jF8YRspkA2V6Ta7FF0c9goQicRvX5EFA9wPEXWyTIYDMjnC/Q7Heq10TGDYCpib2YnxWKFHXM7WTy5SnfQw4QKrX3M0HQM/IDACwh7bahDcTwLdhNB4fp58m6Rca6i3w/RA5DEoAd9nHKPrqnT16MX2E5zkY31JoWiz9xsiZmZMr5vCXxLogf4gUu328FYl1qjgTgJsRHCMMSjTWEsSxR1UL5hdmaepVMLRFHIzitmGR/3qLaauL5PYkcvbEcOPcXRp2M85XB84QSOUsRRTOBluGLXDl7xmldw5MgRoiiinyT0o5g7br+Tt33Xd9Fu9Ln3K1/ki5/8Izq9Br7j0ut3yZbKRFFIt9kkXyigE03qXLu0xMLLq8iVQoa+JJXGNofWTup/HXp0sSIYq9BYwhjMloATpPXOdM/jTDaLARyw9qI+Yc/N4TgeG6vLbNQ6WG2x2qKUECZ9brrqGt71jrfxujtfR5ipcHilxaNfuJ+br72KVx48iIpDFpdXMMohjhK63R6DeIBFkVER18yVuena/cxmRw/CGd+/iOApBz0MLooxKKVQ6RKHQnNmc58ucpx1OZ2xGAUXEVLr1xjAYJN00bzYe/9E0gViEIZUa3V836dcLqO1kM+Nc+jIMdqdHoNBTLPTI4o1zU6b0wunyWQyLC+tUNuscdvrXs3OXePoqEfS69DuNAHD3FQGaxNcb7Qi37NrkvKYRyYzhhd4BCrh2OIiGxKxd2+e8WN1tJ+nMh1Q7a4Q9Sw3XD9DLijRjxKWT54kl1HYpM9YyUMnMD9dwfMdnnxigdqmT6dhiOxo14qV58yJ518/yzGsMfilMeavv4nc/DRfe+xJDl7/KmIN93z1yzz96P3cfuu13HHXO7COxbEaiwOkLkNjLx7LaWxGVNshk5ksZCwnH2uTBxwP8lnBikuMQblC1hPEU4Sh4cabrqF/+AjaCIVSAR1FRGubZPMBiU6IQkMhX0IpFwcXzxktp+WdDtPJJK2VmEajTmewjnG7HHl2lQQPrTVxHBPHCY6UGcuXmN9TIjJt2t1VsA6OkyeXrZDUDHEoiIZ+u0sYK6Qr6DEXtzza9ZbN57F4RFFIba3N/Nwk/W5Ct+cgQUK13md9rcbExAS1+jJRmHD9dTfSavcpjU9hkojq6gqiobtZ54qd80B/qE/6TFWuoKcTlL8+ko7xsQI6iUmShNvfeCee43LvV+7h+NHjNBt1brnjDpbbMQunlomtIZcNuP+ee9m3Zw/L9VXyE2XCOMJEEa31dZpTE+RLRRxr0L0uST8kSjTZ7KX/0Nflda3YoToSwYggZ9K0rGCHPnBtJfWFD/3dsUknkEoTeobumOeUmZxNVoQz29WL5QNoE9HqD6h2B8QmwRGL7zuUMgFXXnUDv/LhX6KQL9AxsNixDLRPJ85y38PHOXJ4iT1zM5TzWTqdNhYh6g8Qo3GURtmI+756D1EvIuxHI+lwHRebWFxxEQ2YJPV5i6DFom2CEIEdBkMk3TYjaT/Tz2boinourcsYg7WWREdnMy1GjovRZ63QcBCilEu/H9JstMgGWeZmd7Lwta+htSUTZGl36pjE0OsNQEN1tcq9zXvxPIe5mbexerrG4vHDdHotDhw8kKYi2gh7EUtH2QGVQolEJ/hOxHq9zv5r9lPJZQnqi1w7DkfpstR1oTzGoBMxGKxRKSiUWyAKNVk/wCSaTCZHq9mitt6n0wOtx+iHHaIIOr3RW+fUTEiFU3i+1WyH/E+sZWZuP9PfNcG9X/kyH/3of+bDv/RLZLIzfO6zX+AP//DP6fXez8Fb3wsmAeOl6YlDob+EWCflUh6b9dA6hFiTsy4l3ydbNHQ7IYlxGGiFYzVZX3BdhdaQzWUpV0p0BwNazRqFIIvjOkxOjiECm7UeruuiRNHvh1TGxkbSYSTCcSz5TMDy0ili2WB2r2WlHrFxSmOtJQxD+v0+3ZZPv2vZvWsWE0MQOsSxxRgX0WNk/CbNJKTRaNNqdYlihWz2iTpNfDU66NrtdRHJosSio4RBp0WQdWm3auQrOeq1kMCbpJCbYd/Ba9k5dwWvfOVrU2MoCHDpnIA7AAAgAElEQVRFEfUGNGt1PveZj6JViDiKer0Dccz4VJHV2jK5TH4kHbv376fZbOIoxczcLjKBz/U3tZmbn2azUeem172Gp5ZO04wTWrU6hXye9XqVbthjeXUJSWIES+B5/OZ/+BWuvuVmbn/zm7nywNV4cQyDAZvrGxRKRSjtGEnLGVxWRd5P7FkLFMxQ6coZjbQlK+WM9SPD7AvBICgUnidgEgapQ32YuKNQpMofubhfaaO2RLMf4WQyHNgxx5vecAfvuOtt7N6xE2uEdmJY3OzyxLF1Dq+22RwIm5sGE1lOhQ0efqKKp0NI+vR7NeIkzYUdywqt5aP8p6P3s3N2DtGjFUZM6vPWpDnw2qRbbseCm1i0dXGtD+pMls7QvXImQ8cKYg1WG3SS4DgORqWLogVc/DQofBFFrnWIRZPomEazjvIDAhRhmG7bHdXnVTdfwyDWRNrQD1sEQZH1tXV0EhH4RVzH8vhD9zNR8ClkfPQgZnp6N3uuuIpI9RFl0Hr0Ft5VHmG/j6HP9OQBkpxDo7FC/nSLHftmUbvmecNMwOaRVTbimD278uzIvoGpiRImYzG9NpubXU6tbXJqaQM8j10zWabF4/h6l6vH53AzZfJTo8X+TJqnnLXMtxam/1jAEcF1x7nttteyc8ccP/7P/y0ba+v86r//Kf7BD91Oxp9AeVksIaJdUi+NTY0XLjos7Jwdw3jCsg7xC0Umru4wNjaGUdBZbtCsNam4Wbq9LlERrKPIBBmq61VcHVNUPu969wdYrze474H7SIwlF7gU5qY53ejQbjeZLgToi7yP8boD13D88UUOPfYkV79qL1PzeabmA2567TSf/8ujHLuvRy2zQmT6dNoT7Jid4djTx+l02iTakskEWCyb7hpT4/MIJcJBn27H0Gi3adfWKezQlHKjXV7N1iaem00DjGh278jTj9YxMwnNVo0f/Xs/zvpqh1q1zuZCh/WjT/Hw3xwmCLJkKyV832fh1CkyfkC33WV6eo6vPXAv2ZzHwasOsHRyE9fP0VodnbWSrYxx46tuJYljqrU6xUKGKw9cwWtuuwHB46mHHyZs1im7wtT8LFdffTUPPfwVHnrsIRq1JhPFIiaJwCa0Dx/mK0cO85U/+hOS2BBnMkxesY9MsUQrjPjkZ/9i9OAMcVkVuRrGMVP5HboJ5Ew6FiBy1r9/ZhubpvZAznfYP1tmVykgCrt84Whj6BKwZxeA52qMniHFYoZMIUNifX7kB36Q219/J8Y4LG00yeTyPHlqkxOLa5xeqVMPodWL6HUjTGywiUYnmrjfARuT87NE8YBBp0FvbZOyGzE+OcV6tYGjRufn+qTuI6VJFyCdWtOuAPTAyaYHnGSoXIa8UMo5m8JmjQUNSlywgklsmopI6iuXMywZAUdpHAc63TbdXhuvXyDIFUAcGo0G81NjhP02g8gwiBIUFt9zyeUDPM9Ldw7aMjk+zdzkOBtrK1SrdWb2XkOxsoPeYI1E94HRO5QkGdBphsxOV8gEgpYM1VMbLD1xmlMN4fqkwMHX3cEN0Sy206fotvHX1gmaa0glIA577JGY22/ayf/3uLDc7bHREfLlAjfcciWPP3SETMEyudMbzRCeyxs/1499ZkcJ4IqDa11MkjBeqjA9OUG32aLbjagUK2Ry41jlYiUGBGWeOxCU4iLBcE9ISCgWfaznMbNvF2Nj4wy0YXJ2F8888CA9rYkkTUEchJr5HbO0O220hiTWKMfnyacOk8vmEKsZn5ih04yY3zmBbW/iDAYYM1oNbDRW0d6AYEKY3pfDzwrN3gBUyN6DYzz1pVWi0EMnFp1okiRmY7NHo1GnXB6H0CGOozQWFEEmk0FswqDfJh5ERDqh24uZCUaPy+TEHElsaTUHTE5Ps7JapTdYp9Wtk82Weeih+zFJwLGjx1Emx7UHb2BluYpITMnJsLZymkZ9DURwdEA3m3DVgZvp97tkgykKuRxaNM385Eg6lDF0mg0814MkhESxe34/u3bMMFaeoBMb3nDrq1HG4gVZosTw2U/+Ef0dO7lq/5XUNzZS74ISHJ3gDd2mrgKlY1aPP0s3jMhPTI2kYysuqyK3Z/0B9mxeqT0zWc7Ml6FCH2YXohHGcw437chz3e5JJop5njpyaHh4SD+Xd34mi1y2pnidH1PTY0xNTbJ7537e8da7WK22OHF6g/HpGdarLe5+4DDtnmbQ69HqDhiEqT/LGEFHCVEYgefgeh7dQYKykMsYykUfJ2zwlXseYWJsCscd7Uqg+QRiNKIcjE3SQHCSpItZto+NfLRpgBOC66TpSCZDFCUkSYJSQiGbxXcdPC/drg/Zm/JP0owBJQ7pr+GdH/EgpF1vUK9WGQxCwjCiVq3Tb7fptGpkfYfBoE+93ceKQy6bZWm1judl8X2fVrNDKe9BlNBY30hPtiqHmZ278AoT+L6PClvocPQvwqmZPEYnqEKG9Y1FNIIOhX7ssF6rU/QGNJJ19r/iSqp/ej9UTxPs2cNgfQMTC9YkdKqL+HGf109McU+7wZeebeKWI962dzd7D+7mxKlVHntktA9URCGSCqBSz/3qmADK2jSgHMc06wusLx0nMpsUK5O8867bWF7eRxQmiCogbgbHgrHecOd57qIwWk47YZt8pYjyBGsVNhJWTq3S15rC2CS7rzvAM4eeBQe8GOJEs7JaozzmMrdjB0o51Dt1dBJTLhawVnBdj4npHAOlSHSEVj4rtdFB+WqnSlDMc/Ur97JUX6FkslhiPF/IT2ZBpW7EZGDoOQPa7TZB1iEMQ5qNBp7nIyL0+31sFFEslUh0RBgO6PZ6aVkvZNAfvbBNT+4jCiNcN0eQLXDFvp20OiucXjpJq91BkZDP5elHEZVCiXavhRbN0uoilpD5+XnCsEOtUWd+ei87du7kwP79eL7HZq3K6cUFsvmA8RsvfCgJwNEJKo4IXJfve/+7mZmapJgv4Lvp+Yq+DGN4sabdixhsNrj1xpt415vfSnZqis/85V9gkgQcg++7xElCFPbxHQ83sbgWvDhGRZf+frXLa5EPg5tnFM4Zq9vCMP1OtnxPswh3lbNcW+6zOxeRURGHjh7jCw8/g+QOYAxDvzCI2DRlB7iYCfre97yWOPK54drXsbpR5clDJylMTrFSb/DY04ssr7bo9QdE3SZhFGOMxiQxcawxicX3A7RJ6HRDMjok73bJOm3ifpvTi8/S7XbTeIAzWpEPFj4NCJ7nEZsErCKONJnAx5udJjmxhOM4iJNmsjx95DjtWockSY+PZ7MZcpksge+wY/c45WJANnBxlMJ1HLQNESukP1Jy4wXpSAY9+p0mjdoGSWJJEks77KLDmEGU/iXaEkUR4ngYY6nXG1QqFaIoolqtMVHaxdrSMsfzWdY21th30/X4uYBEPJxgCtfLobcoxfPhwG03kjewdOQoJVdIYuh0unhBBreQYbO2QmfpNJmZIouDDtKNuHp6LxJUMHGLQsEDR9FqRkz6y7xmKss9R+ocW6/xQPZhbnrTa5kxBU7dN/qnEGWYBbTVtSJnt5KWdnORzaUT9GsLJPEmxYki2fw0t77yOsLoIMYInhvgug6OMYh4WNFstcDVJTjJO2GXYqaMwTJo99nYWAcDjY6lNNXlzvfcRtP2OfnYCXqhwSSWfKFILqco5PNksz5R3GZmKkc0AOVlmZzaQZS43P2pz/Cd1+5AK8tK7WIH1yLCRJOEmuyYR2xDgoxPYjSWNuVKFt2LCbsdPCUkSYHeZpNms4nQxfd9HMchSRI8MdAxhIOQMOrR6zUJ/CxxrOn3R7s0fuLH/hWZjE/GL+P7BZTbYXHxCE89eYhSeZxCdpp8Icddb49R4pLxc3SafRqbdRqrazTbTZr1AY6Tp9PpsrK8wtzsPK7rAS6OA66COBr9exuu1YwXC+SzOeZnpygWK1gDOjFEaLpWpxvsQUy7PaC6Wed1N91MuLmBk8+itT6biZcIJKSncMVx03OicYKv0nYuFZdVkZ+dDEN5tpKmx1lrCC14nju0rNOUQ0cMr50ZUHTS4+mPHXqMRx4/TC3Yg2OHrhrg7PkndTanYyQd+6+c5L57F/j8F+7lpld9J4ViiTAc8PAzxzmxNKC2VqfXbUISY4eBwyiJMAZ8L0M0iImMJkqgnIuZKSbkHMsjDx6h122R8X1qzSrz8xMj6bDRZjqooUJbD60hChOyUzOMTUxx+LGHUALa9zlyapEjxxYYz5QJPA9tNIOkQ9x2EBFq6yfwHPA9yBd8JiZLTE35Q1aMzkrot1usLy+zsbKEjg25XB6rXYwXkC9mUI5L4LiUSkJiLFGnx9RUhUazgTUwOVlCFPT6fU4vL5PEMXE/YrO6RnFqF5nMGCgfJ1MaSUev06FVrdNc22Rsboa877HSWWamPMV3vvt91J/4ax774gmqvUVOPrbAwR1l9oqHjhXRRpPGcpuw02TQ7zE/P8NYzmfScXhoqcZxN2T6OzS+X2D/3lE/l5jKqVLq69MDBawYqosPYvtVMr6BIEsul8P3c+Bk8HwXwUknqTHI2Tz05z/vUo7o+4WAhAjP86g1ezjWYbMa0mwl9Dox9XqdgzdeQ32lQedkHV8J111/E83qccJOi6mxWUrFIkV3N4ePrhDGMDY+x6c/fw8PP3SIV+2dZBBGVGu1kXRMFqZpt0Miv8PEWAFHpdk3cQTixmRyAe12i057HdcTev0ig7BLs9kgExSI4xilFFprfCchTgZ0O11WVpaoNzvMTO8gEYM1oxf6nbuvTVNxdYAVH0OGPfsq7N3/ekykwLiIC+KkZy6iXp9KxZAJ1tAty2OPPsvc9F5md+5AkoRBGFEoFpmb24FYw+R4FiWa9bW1kXRoNEunT7O5UeVP/3wPb/+u92MMrC6dJJGEA9dej7IK8dL3BeXKZVbqNTrrbebKBYzWGHEJsi6dbpfYWOIEEjRGWeL0fCiJGe2K3IrLqshDPTzAhEWUSVMFo02+Y3cOP1J8+sHH0d06g26LHbNj/N33vpPf/4OPMDNT5hOf/BSRKpILXMZ33cANb3wnxqaZL0rSI/3JMI/8YpPk3i8e40M//19xC/P84r8ZZ2I84OTJGicPLXJ6tcby6RpI+i6RNFvS4ngu2hh6GgQHq3V6QmvO46b9sywutrj1xoPc+/AjdPp1/CCg2xs9EN3IUCyWqdVquL6H6/js2ruX00ur/Pbv/gGlkocjcOTpZ+lEmiBbxPEDnExA2OsSRzGOE+O5ChOBiIMKXRo9YWmjxd56kYnJCr4/WpE/9chDLC2scvLwEtnKDOXyDFob1tdWQeDoidOMVUrs2bsXP5PBCbKE7Sq16jrliWnWaw0Qh85mlcQR+onD4qkFrnj2aXbsuILIJsMxGU3HiceX6Xdq0BO+9tBxPPEo9fq4Y1Weuv8BbpoNyA5iSsrytje/gp3zOzn58Och6tKuVmn3IjobNQ7umefU8ROcigs8+VQV2/dxvWmOPHJ8uJXOjqRDKUX6EjFJ33VCelgL3WNj4X7KahNbLBAnGmNCRCDwi+AW6PdDXMfFcTyUqxGVHrO3fH0a6MXkdGbXNDiCSfq4Tki/F5JxNPtmC9Af8OBfP8ir3/IK/t4PfD9//fHPENdD3njXe+icfopiUWi1N+h2uozt2AdehdPLdb7wpa/ysT/6BLNjBY4eX2RhaY1bbrhqJB1GNRifDvCDAtomgCGJLY5yUJJj9/Uhh5qbDFqKql6n2aliIoNSLlKW9NUR1qKNYdByIeOyurZMbW0V13VwrSU/ZQns6B+wXzydBkOVDLBeGlZyEGwyoNta51RbMP0euwqKYi7HA0efpRnXcXttCvk8r3/Pa5ko5XHF8KmPf5m73vce3MCjH7VRNiE7lgcLu8qjfyFwdtduDh86goPP/fc/zo23vImxiSnGJuZxnIi8I7jKod5qsrZwnIk9u7n+9jdwcvEYK9VNBv0eCk2iE5SJcS1oo4n7EYgiEktsNU4yOl13Ky6vj5wzpzjT4EzGhiytrvLpxxcoO31Kp07iXHM9gzhk8chj/NnHQzabVY4uLmMyk0wUCuw6cC0T+29Bn3nRnE1P3NkzebnWnPVvXgj1apvpyVlq3Zhy2eWqA7Ps2bObOFEcOXwEE3ZITIyV9OitchRRBGcS9I0RnCQm8DSiA5595in6ITzw6CGM6+P4Cj8TYGW0hVEujeG4fjoZdl2JCnzylTK7CmUOHT3FkWdPEiURNra4bgbPC4gig7UxOoEoihFliRNFNshgjcaQYI2DFcWp0xvUO33GJ0YL5vrqCq1mm3q9g2sd8qvrZHM5coUCWofM7dhBu1lnZWWZysQE+ZLCU4Z84FHKlxgbn6fR6eK7LstLS2gcNhtdaus1us0GQakM9vn+5vMhFijOT2HagpyokiB0yVHvDjj+yD1kD87xtlfdREHHLJ06hasU/XaTvft2MTY+wfLKGp1On0fXG+y/8SCLRzZZ72n8bAFlBKXhq3d/ib3794+k48yrDkSGqa3i4OAz6C8yGGzg+VmUBASuwmgX1/GI8UkSA66DF7goq6lWl8lVZlOZNwp7Ebk8F4VigVq9zmAQE2RdumZApVzEJopYG7IYNhc36O5s84rbbuH+z9zH+MQ8dDfJZCyRVuSKLl5mikePfo7xqQqLn78fz1NMlAvUGm2qzQHX50fvlBzXwaKJE43vZdKAvKsRMVgxTOzM4JQsYd8iVmEHllZ7k0zGQyQ6e0LR81wGTosr981B1qNR88hmi2QzGbLZ1KU4CjoZul5V6oRWFrQxiAHrVzi5cZKk32TXzqs4XW+z0o0Y4NBcb1LwQho9yHgNdu+cpzK5E1QecQMcZcHEGBMNwxij3V6e66EN9MOIdqdPElusUZQKZXJZiyfpGRB0go775LI+C7EhDnKUs0UYpgYPtJANfLCC71psolHKwziANcQXmS9bcXnffigWURZXBGUTYvGYuOJaZvdcRUXXeHvYovDWN1ErVfjEp/+SBx95mJkb7oTFFV512y2Ux8axjgcqPZBxlud2+HmY/XKxE3NR2OP1t93KUq3BF/5/9t4sxpIsve/7nSW2u2fe3DMrK2vpqq7u6uptONNDzpBDisNFJEX6QX6Q/WADtiA+eBVgwJYgg4C8QTYgQCYsa6ENmLZALeCYizCchbN2T8/0xl5r6cracs+8mXePG9s5xw+RVdOk3JljwGiQQH1PhUI+fIgbceLEd/7/3//rX2eq+nM89dQ1Ll9cBpeDyxGuIM9zjD12nCqBEAqBpMgtvjDUI48Pbt7i0rlV9va7HPWGCE9Tn1bkRXFqH42pGbI8Z+XsRZwKUNUmojFNWLFcfv55dh48wJMRxhMYa8mzHGcE0lnyLEZJh689nHUUSYbFYXC4cntOLiVmNOHspZN3XN1un6P+kMP+AFVYOodd2u02V568hAPOXbzAnQ9v0NnfRUpIJwkrcxUqlRApJFlSAIreMGFqYRWBh43HjFNH92ifqaVVlFKnGrV0s8rChTnuvHafyTgvfQSFpGMFs3XJxuYee1M3WJtpQ1WD6VL0+9x5O6Exu4gva9j6NLRa6M/9AueeSXlh5gZvvvIOg+EhxX6ApwJa9ZN103+2Huqi0nSIVgLPm8ZX0wghSSYdrEmoVeqk1qKkAJPT7XYo8rwUx8pjs5v4087c03bklajCUbcPRuFXNFElphKFxKMM5UueevoSneGYe/fWuXj5IkorCqc5+/RLDLq7qPo8zalZPL+Bfv07XL7yBGtrb/Pyq2+xsjDD7v4uh3HBN19958Q+lPKPZb4Ok1oQFq0d1mYU5DRnaiyeb/L+wQFeVqdeqRFN+RRmxNFggFYK3/ew0uPZzy1z6bkm6+8b7rzvo1WFwhR4viIIT16OpPQeqYbc8Rw60D5ZktDrJmzc2+Hmez+gLiY4F5Cmgu+/9S4yi/nVX/lVosDnzvpt/vCPf5cLrQWecz7W+iX2SiiMUD8SzCxPJpiiwDpRmqGKAnPs3FWqHDs5a6lVK8zNtMkmE/xqndmKjy8kUmsmuaGvPYxWJIWhMJbcOCgKiHxGWYofnPzl+NH6hOWHpabZUnJVsBabTchtzva9dX7uuRc4uv4223NLeI029cUrTK89Ta8/RkqB8gOsKXDWPXKBAj+Uahz/nzhlRi7IuH3jA1bOn+WtN64TUeNwN+bbr73J3t4elcoUxgqcy8mzFGcyvMDH2BxTOIrCMt3wSZM+k3HO5sGYTneEERpX5Ehdzkkf7iA+rqrNNmY0IE0MveEYmxScby+gfbj89JO8/Id/hNSaOElRSFxW4IUeeTHBugxrCupRiK8CjoZ9UmvKgxOT4UnB1OIsn/3sZ/nUS585sY80zekNR1RaTZbXLjAejxmNxoyThOmpKkIKWtNTKJFTq4aMBkPigaUwjo3ND5hfXmPv8Ii9vQNWn5il1Wgxs3YRGLO1sUmrvVhKziTQ+njy4MXnniHJOuAUNsnQgYdSAYdZzlEGrYbEJ8MEPtWpJs2ZafxU8f6tTd7bvsXSyiJXnrzI3e0DPuxNEMtTPP3FZ8hkxvobb1HNF6jVa9y9c/fE6yGl/CGh8PiA05FiXUoYhITRLErPUeQWEw8pigHZZIwOI4os4ehwH2ELAi9CyXLHJfjTKDf7IxCRDg+OCPyQdOJIXYKTBYUpGI5GRIGjWq1za32XQ5cwuzzNzHwb5UXIcJrWUhMw5BmM4/Kwcjjq8cQTZ5hu1gm0pDsYMcoM8d7Jh52BH5FlCdYYtFCY3GKtxDkNTlJkQ+bmIz6s5Ix7PWxuePHnzrF0NuRg44gkTqhUQ+r1Gueen8avD1k5P83imR4HGzmTLMH3m0hxyoteaayxWOuAUt2VG8NoMODezXUG9za4++Y7vKENl688i7Q+y9V5ZC3n4KDPg40Ntra3Wb+/z7Pnr7HXG9KYktTCCp7SuELhKJEfJ14PDQpDUthjzLBhf3+XrQcdLl48y9xMG+EMv/+Hf8jv/vN/gW5N8Tf/4/8Cm0NqMkbxhEGaIXKLdBMKV+YtICSWnKrnMUgKtDvNuPaRa/Mj/+X/D2VLKvzxXFtgkhFx7wBpCyLP8ebSZQ73OnR3EsZimuVrq3gSzj11De0HYPNH55muRMkB/JAFDODsqfCdCxfW+J1/8TUG8SGXnvoCd+7s8P77H3J3Z5Neb0RuoFqJ8JWjO+xx7uIF8izh4KBDUbiS1a00R4f7ZMZht7s4m5PbnEqkAPVo935SVfyAXpZgs4zCKKqBYv/m23QOO9SiKp4WpK7ACYstUkIF1WqV2blllHKMJyOGhzHK85DVkPFRF0F52r26vMy15y9y6YklkvhkedkkSZmkGTPzC/hhhPYCFpaWMS6nO+hRjTTt2TbnV2e4v34fTzr6nTGjScx+r8fUTJtkcEijWsEUFqUUfiWiyC272wcszt3H8zRKOS6e+8LH9rH99j5rV2ag2Eb5pSQzR1A4yWGmuOQMu6MB450d/Gqbg84RHTmP96lPc7k1z4dvfwO7fpdqf8CX/q/f4aAyxdoLl1h8+iwVT1BtztEdHJCdoo54eDAHx3pySSmLlBaloxJcJg2JMRQOjIvpH+2QOZ8kGeMpQehrsOXBvXMO4Uz5byg3M9aeuvvrHw6oN2uIokAoRwEkeYEfetTqER+8d5u9rUNIQvpHByALfC8qQ1isRgiP/mCIcIIojOgc7NCo+lx98jyuKFi5sICcTblze+fEPqwp7XjGFhiTo5WPyR1FrslTQ+Bb5mciLp5f4O6oR54WyGjM4oUGi2cXSSYp8SQmCj0GWRc9soSVKdYutjnY2sLZkl0ThifryK0zOCxCgOdJpHWMegOsyTi/tMBMrcbnr5zl3OoCXrVGWJtBRzXG6Yj9eMDa2XMcdft0jro8eWaFb37vVbrDAcuz83zhx1+i1awd+1ZOVoskowEKi6JkkltnODw45M6H77C3t8uv/Mov8bWvfo2/+9/+92zcvkMiFa2wxfbBNkWWcv3GBxjhMRY+RTEpN41SIoRCeR5JkiN0yP+XALdPdkd+zDeUOJQoDxBlaxpP+Ug7x3onw8oWohVQPR6ZIEoDgYPyYXAPKYnumHJ4bC9y7tHnljvlzS61RWpFnBquX3+b1dWLmEwxTgxKSeLREIzBTGLS8QApDKF2aJtRiSLSNKPfPySZJGRGlFIn7VDaoQOwRh6nFZ0yaxOWhXYTB8TOIxt2kUVCpaFRWnJ2dZ4bd+4f8ywNzVrE2vwUi2dXya0hiFT5OXn7ARevPMHBy9+jEUQszc3z6eefpt52bN7/AKlCXjyhj1GcgNQElQpplnNmZQUpFTdvvo8pYs6vrrK0vEo1cDgn0U7y2ndeZxQntJpNkskQpaARlVrayXiEabeoVGpsPdhlf2sbqQTeKZ/Or//f77H9QbO0jytNlsVkhUEJxVHqKFTI3VhSGQwJ8j4rKz5vh+cojEfTKrYPBboxS12nnA88NrYzbvbvcv5ZSbsxxfqD28wuzKPs6bPqh8lAAoeUDpMbnJVUqnWOUouZdFFOo5TFFuD7itGoV85Zpcd4OGSqvQRW4VyKtWWC0MPD8x9FtSKNIB3GBMpRnW7R2e7T7cWszE4T6QrjyYhAamQuGO0dMhqXKispy2tW5GWqiMky0tGEcdYhUB515cjSglRlrJ6LWF4++cwgmQyxxuIHHv3hmFpVEMcpWpf6eKV8orrHhcvT7N1Py6SptMAXDqMcYQRS+UA5OihSA75lbqWB0w9Aa2SlXA9OKnus4lBaMz1Vxfc8ZmaaUBikU5g0Q5iCeDziKB4ynvQ56B0xyccYARXPpzI/y9L8Iltb97i/u8X9rQ3W767TrAZ88ad+CmsN8pTn9qjTBedQ0mKlj5EQ1EKieovMCHb3O/zmb/5Dbq/fpVqpoLOC8bjH2pll7t67hzEWlIeu1EgGSSk9FOX9oT2fojA06i1m2yer3j5an+hC7tsM6x5qADRCad8tkrAAACAASURBVDzlIYQFfJIkIc9T8sERUmuMNRRJynDQx9Oa1swsSZKWwJo0xg9D/LCKlJDlWTlTsgZTnKwWyfxdfvIXL/N7X3qL3U7OZ7/ws2TOEkvB/XsHKFS5s6xXObd8kYNhzFGnh7SawCa4bEw8GoEtEF5IVJviyQvn+PB2ilSW3EJRGKw5+c0+f3aKvKgglaAa1cAsgTNkaY4xjqvPPcmN6/f4rd/+HTb7Cd29mF/73Oe4t3mdaKZGs7lI6+mzJGmPq2sLNOwzbN29h80O8XXGM1ef4s6dDT54//aJfRwOcnKryY76XLv2NA8e3OXGjes0602atRq3PnyAUiGzM1NU2itol9Gcm+LauRfQvs/m9hadUcHq6lmU8ImiiMj3mZudod1u8My1p4nqNcRpMz+bsrM+QqsCmyT42hLnEwrhcWjhj+8Z9KYgxvCzixEvtJf5p//7vyLwJP50wMr0LPevw+f/7V/m8xuvsvpUk87Fs3z49jZ3rndZW1tkOBYMJifr+x8utFI+dDZYgiAkDyooz6OiQu7efo88GVHxRanlDyss1s5gTMrdO+8QBgW57R/T+BKkOYa7HaMpHtb09Mc/rDMzs/R6XYb9ERqf2fYiR1mPw4MR494hUaWCImCwn7N7e0y9FfLhzVc4u/YUzalZisxgsgmjfo8zy+eQdpZvff1reGJIt8i48doW9Ujh107eCR/ux1jj0Dqn2YpI84yw4uN5HtZAGFTwpGL1fMD6mQGd/QFx14eUchxpDMppjHHkE0VU9SiKEVHTsrBcITOOudUQPzz5eSlMhhSCIs34k++/SWEnZEXGOM7px2OSfILSPkJqclsqa8ZxRmELiqw86SgsxEnC9lGPBzubSC3QFcW3Xv8OP/0zP45w9jR4Kts7O9y59wAnyvn2d76+yhOXLnHhzAKVqMrf+Vt/i1defpkwjEBplHZ85atf5qe/8FOMhj1GwxImF4+HPEQ2SCmwtqAS1pBScOXpyywvz5/cyEfqk9WR48pYt+MZpMkzbJ4xGQ/p7G1y5/Y6WZogXclYKYzF2RysQ0uJkYpxnKOFBJcT+rI8iBGCLCvAD1DCYk0B/+mvf2wfu3tbfPHnr7G0tMD/+Pe+gRAeV69e4/zFZ3jnzTf48u/9PlL5NGbXqDdq1FstKtU63YNtsmGHyC/t8FGlxjizKC2xooTKl8gBibVFSXU8oYp8RFGk+FLjjAJbMpnLRCCLF3g8/fQqf+1X/xLfe/MdcIr20ixGxyyfm+PS1ctYIZidrfDWm+9z5dIqZ+bq5Sz0whK+Flx+Yo1B72RH5fz8IlGtCVJTrYXMzk2RpGcQDoo0o9/v8f5715lqNTl3cY0L51Y498RleoM+JCnjNKNSrWBtUdIbRUSSxvT6R7RadXLpsEXGaaKNF74wz8FeykLrKrWKZWv7Hnt7u3T2+5iiRAk0IsGVzz3F6MEmvcmI5acaVMMKK1fPU69LHnztBl/+va/QfKpB9aiPT8b47CKjo5xXXnuHFz/3EpyizzXGlPp+K9DqOMYLh+f7oIDURzpLlgzBaDzpIxUov46yIfXGLElyhBf4JGmMdRnKeqVvwtmPtf//2crzvDwvMIqjgz7W1Xlwr0cz0mSZQ6vyPsmSgs7OiIX5Ke6s38ARcU4o8sxgixIrHI9Szp9dIksMEiiQ6EJQdZL8FJmsyUtHqFZ+KTKQD6PNSonheBzjSUUUBLRnQ/Z3D0kmpjTQUQZqlKx8SNPxsbQXsqxMyKrWatTqIeq01chJrAOlPb7+R99glHYYJiMK55EaRy8ekuQZeZFjjCpNOsaUR9XeceastRSmwKBLWbESmHyMbLXIAS0E+hSGv7UFSToBqdm4+yH/8B/8feqNBu1mkyiqcevOOgK4cOEiV689w1tvvsnu5n36/T6B7zHdamJyQ5pkJbNHqeNxnqU9NUPnaA/ncs6fP3PKBflhfaILeZJl2LxMDNm8c4ejzh75ZEwymSAwTDKD1qXELzcOkxkE8hHuNsstSmpykaMUKOEhsHS6w/JzbpyhfYE6ZcV46vIFanXDL/7CZX7rH3+D/b0NmjOLBFFEreoz7u/heQHjOOZg9x711ixzc2dIPE3mCtIswRjLZJKWEW0CJkmMVCWO1+alSwt38g3h2QwtLZEW5CbGFXk5PrKSLMtQXoBUiud/7CJXn7+Iy3IQmqDRJJ0MGaRHaFVw8fwcN957j/HgkKtPX6Le9NF+gXUTtPL41IuXT+zjJ3/yc0T1Os2pKfI8pd/vc/nJi4yHQ+LhiFe//zpZkrO/fYhBEkUV5mbnOegP2N87oNas89SFZSaTjK2tfcLQpzAJo3EP62bJXRlOLE4ZJ3jRGL/isMLw7//1v44nDVv79/iH/8v/yoPbmyxP+Wib0prXHB1o9vtDfvWvvcTedsrACpprDcJn63S/ech3Dyc8cRRRSx7wQTqhayGYbTN2CVmen9hHmdlZumeNKSmUBnEMulE4a9CqhI0NJiOyWLNw1jAaHiGEJoymyYsCKf2SXAiPRitC/PDB/X8Ldv5oZWmBMSDwGQ0mSJWij+P8WtMRwjoqKihljSbHVx5/8ua7zM5eoN89osjLA8Hu0QG/8Rt/j1/82c8xHqYoEZHmYypaEkl1Oi3UZSW3TWrEcd9JkmCMh5KWdFTay4UzNKd8wsinKATjcYyVKVKUZjchFFIZHA4tA3ACZ+UxJ0iRZif/LnBs1hKCM2cvcOdBzuFgRLffoz8ak7qC3BqMs+RGHiuGSrtgkWc8jIAGh8kLjLVlClkREs4u0u+OafjRafsvZudmub+5RZIVYHJGg5ThoMfB5rG2RyqUkPzE5z7HlaeuMBoM2bp/B2sti0tzTCYT+r0BaZKWJFj7MAIT4njMcNDj7vptPvcTnz71ejysT3Qhv/HWq/SPenSPjhgPYpSEIJDHBiGF1h71ash4kqCsIfD8R5Z+pwVKW6ZqEXGalEG6viSqeBSmgq99pE0JqwF+cPIvMTtTyqnS0T6//CtX+fYrr7Jz0OHqtZfAKTACqQzG9cnyEYfjLvFRF1tkOJeRFgnOWApjCGpVrHFMkhjlKYzhUUqLO+WwQmJBSbKiACUxRQHWoCRoYqTTCKkQvkDJHHcMym/OLzIe1JlMDokqGdI6nr2yxqvf/QGv9bo892MXmZrzsNbHOY085Vtxbr5NWIuIaiFROMOyXSSexExGYybjMRsb23zw7g3yiWNhdYVvfed7XDwzS7VeZfnMGabnZvA8H6Vj8jxjNBqSZhMQBk/Dw9Sm0xby66+XI7Xg7H3CYJZ206dvOlz9scu8cO0ldu69zv72Pru7ZbRXZlOs6iGcR3crptqKaK1MM6yOGM77bO1bFpIhU4tTzC0v44cO4TT5KaO34pg17ZzDSQt5jjEjhB2SIxgNOvQGByRpgnTQH6WEYcTm1ia+P0OtMk0kwOQpgZ+SWQ/7kUXc8zy09k7V1QuhMEVOtdJgvLGPUJIoCrh8bonalM/m3S26h2PqNY96S+B7mle++xaXLz+Hp1SJqU0m3Fn/kNFwyJ+8+RbnVuapRrP0uodUI8H8TIWN7smqldl2E2McWimKrEQjSyfKxZDycDuMQhwWzz+GAFsY9ieoMMe5AikLtPLL2DtniceWQVfQ76cIlZFNHKP+6U7GhwvfT3z+Z3mic4X+6JDt3R229/Z47+YNBvGI3BSM0oLclNmaxpWHtA9930opinSE9jwCL6BVrzLTnCKJMyIRoNzJv8uLLz5PVhTcun2H/mG/jKmkFHFoJcmtI4gCrLV89atfJUtTGo0GCwvz5X2bTMjTBF8rMmN/OMpTkoP9HTytwMLd9XunXo+H9Yku5PdvvUea2pI9biGqaBqBT2aOP/dsgScdCkPoS2aigKb2UEFAph0HgwkCRb06zTgd4AUCoQrqVZ/ID9CFInOO4hRVgrEjhK2SZwU/89NPMDvb5vf/9Vt871sHtKdXESJE4AhUQlYUFFlOZkrlhxPlTk0IVQbMApPJhOEox/O9MqQYQZoXJR3thHLOkucOPwwospwiy3DGEngCX4MzDmuK8sH3JIUUdA4GzLSWqNbrJPEhWoEwCTPtBmdWVtjfG9E56DM9X54neJ7BO+UQqVIN8TwPBQS+LgOGFXgSAk9x+Yk1djY3SCNBv9+lP+hy7swMyyvLhFEEUmANNBsNxuM+2JKApxSM41EZ0ixOD5JdvnQF5xLa89CaDfjGu/+SH7zzZaabbf7dX/qbDDpf5Jvf/jKv3H6ZStvD9kfs7HbpHSlajRlkkRHlFVwumLs4jz+r2R1lrF26SPPiWcYS8rQEkJ1UDxnuzrlyNIFBqJxRfESWpnheQHtmBfD54Pp1JBqpfDqdI1qtKrMzy+jAYSaHVGsBMivVJp6u4Pv+MXtEPyJaflwlk4LhMMbTFq0USMny6jxT0zX8eoH2cpoNSXu2QbWl8LRiff0BL3/nu/SOOszOzhDHY773ynd58omzBNJSqYR0D3KUCjHaoiJJOzhZV98MykPwPM/xVUAQBBhjSNMU7WvG2ZhJ6hDCoWUVV0gMKaNuhWhKI6TG8zyywlLYrAz6ziPur4+IwjoKQzEWDzU9J/4ujxy3QUS1OUNSZJxZUczMzrK4skx/NKQ37NOfJEyylHgckyQTsjwmTVOMKZhMElQkmZtbpD09y8ryKufPPUOjUi+f2VO+lFZWlvkMgtm5ef7oy18jy/MyMkBplpcWuPNgg2qlinWO3d1dGvV6SYTc30e4gu7R4aPzFy30I6mrFAKtFWEY4nsh/d7JWN+P1ie6kBdphgZE6KhVFKHvsbI8T+egR+eoT5ImhL5GmJxxkiOEY2MyRmlNNaqUJ8pKkueOUWKIigbSWXqDPlIc7+yFxJ6iWoljSa/bxw8ky7NTFGcnRL90lf1Dy8vf/5DGfIgrYpwNkVYiCSjy8kDV2hycRfsBUmuQBiUMUvh4KsRTYGSpdUeevHCNuhOclLhcoD1LNp6QJyk2jEBKKk2J8jVWlCqYwjp8reh27rP1YIPllRrVQGNNQZZbnnvpWXa3E0a9hElXYLUhNbKcv59QXhigtcbTHrnNS1qdkvhRiJSSZ5+9ypOXLzKIxxx0OhhjeObppx9Z2a21CF1+hTx15Ql2tnfBQmu6xfLKmRJK9CMs5NGqRxxLjtId/o9//V+yl9xGt3O68Tpv3Pstrr/9gM2DXSpRjdglpH6NnbuO6eYCg2GPIMvZvtmhWPAwxqfy9Bp+7rO712Nn0EdREMcDXnjm47XsALVayQcBUDbHOYmxdZAVijwl8CM6h3f4zuvfZ2b+HD/z+Z9DSJ+XPvPTFMe8HCUiRLRA7jSHRwfMzMzRbJUmovIlIU4drVjjMYkLjD/hsy+9yDieoERO5FlG2ZBPf/p5du7vUanV2Njbx6unpG7Iv/qXX+LDG5dYO7dMHI956623kUbx2U9doHBDEpUy364zCGZImxXWb+2efH94PkpqdK1OHMdM4gnWWjzPwxlHJaiSZzl5UZSzfCKsKxiOLGFLkU5STFFySrPUJzc5uD6Lqx6XrqwwGgzJ6CNOkf09VPo45xiZFC+qUG80uXXrJt3uAZu7B4yTmOF4TJJlFKag1+sTBQGNqMr03CwzMzMsLS2xenaVwK8ShU08XcHzo3IsIuC00N/d3W1G/R6LszP4SmALgbElW+f+xgZaK44OO7z15husrZ7l1VdeIfIDrr/3PlmeYK0l9L1yJ/8QFnhMbfW0wvN8nMl4/603Tuzjo/WJLuRmkmB9D51LgnqEpz329zvsHvQRTtKoV6hEPs4mFM6Q5BOEDolqDZI0I08zjMuwxpIVhiwXOJPSOeoipSoNA7b4N3Chf7ZcIakEVcLIY293j92dDtu7Yw57jsLk6MCBVuTp8YMmBIFXReYpSTJAewI/DJDKQwqJH/h/ivBWGIPS6tSMyixJkb6HICrfzsojI2U4GhNWqmR5isLgh1WcE0SVCoGqEPcn5HMRnpeTphla+jghGcZ92rNtPFklz0YUbozSIPyTvwykVgglQUmEPJZOCtC+h7UWPwpBQNODeqNSXhJ9rHvVilB7j7S3y2dXaE41sYWlUq9RqdfJ8hztJPqUEc/uRh+/BYvzOW+9/Q7Z2FCZqyD9Frd1Rjd5j4W1NvfeztjcGbLmSWKbkQ07yAqE9Tnq8zkXnr3A4Chl7/Y2XhCVwKeeYe1sBWEE77zxDvw7J1wPqdD6eB5L6QvQLiSoNCgdngmVxpDFpQssLS9TqVfIRM5w0EOpkGqlThBUUcend1PTi4/CPo7D0o9vq5OvRzzO8P2glNi5AmNTFhfb7Gys4zcD9nYP2d48oNoo2OuMidoFrZmA+coS9VqNm9c/JAwVSZLx5PmzhKFHlo9I3Ahb+IxTQT4qSDn5Pi3yHCkVWZYxHA4xxlCv18tQ5TjB0z5aanKTUWsGnH9yjoODXerTPkKYRxjbEibpUNLHWoMfFhjRp9IMEUISnJJsppR6lH7lpMVaQZpNyPOY4eiI8ahbRqtVPBqNEN/zmHvhGWpRlWZYhiP7vk8UhQSVGp6uHsc+RigpMeIhFvvkunXzQw4Pj5jEKUWe85CR+TBnwVkHwnH71k027t2j3+syVYuQEjylUb4qndp5hu95KKUfHYKXnB6Fc2Dkn1No1o9XQnaWFrDxmLDVIPQ1hc1JsiGNWoNxPMCYmEoUUK9XMdayeTTGc9CLx5xbmGdnb5/OYIznC9pTTer1KXYHAzzfpzAGA0TRKbAZ51GvNahWQ+4edEgzwfbugO3dlNHIolSE71eQIseaUsZtCovWCj/UVGs+UgUgPDzPJwzKuSeUjj1TFHhBUDIhTmoDyySZ4AUhUoc45WOEJjFjfGEYDg+p1+skxUONrcT36sRmSKALICezktwoRKFQKsCahEa7RprXETbGiATjTo6uUp5GaY1UJWNdlHqoMtzZ048WdJTAGoOQsvy740O7IAjAWZyzRHmE0hLhBFL7xzZjgXP2kcnm46rT6bPQXqZ/lHNvPUR7FZpqgSeeusL33rnJgjH4MsOonLrfZDgjqMaKoFaw+swTSE9SvbBMra2YZILBqEvWP6A2vYBDs7t7wExjHuedNlopMbYgfsjL+QjS1g8qXHn6Ga5ee5a8SLl16wZf+drLXLp4mavPXCMIK8cM+DIz1Zh/Uzd+2lgFIKqEtNoVoqpHmiZ4ugRFjccFQaPG3fsbGGNoalXyxrOcoKaZFANuvnabSiA5d26JmYUp6hEIa+iOhkxN1dm4f0i4GLJ6YY0rP9E6sY9xPCbPikf3eBCUX3DOOaIwQgsPYyzVsELuG7QSBLUaXhSXYDuhj0dtGs8DravH46sCSw5Wk+eWJDvN7VqONJ2zeFLgTIYiY6bVpBIo1s6eQ6pyc+HpcpwThgGe9tBCo5UGFFpr0DWUClCqHKWUsuhjf8opX479/oDJJGE4HOP5Pi4rMOb4awGBOM4kTuIx6SSmEvoEvqYShQRhUOIz8pzJZIKSEj8o5+nymJAppUIpRaX6o1v0xY9iTHhcj+txPa7H9ee3fnQP6ON6XI/rcT2uP5f1eCF/XI/rcT2uv+D1eCF/XI/rcT2uv+D1eCF/XI/rcT2uv+D1iapWfv0/OuusKxM2pFYoIVBOoITAlz44iXM5Qlq0HyKVI54UOFHBKFe661yKyR0T62NdhhAGh8FaSZ4Vx3ZXx2//o92PPXr+D3/9eSelwMmU3cMCJWt0u0dMJgmB9kl1xkJrlsPOCM+TjCcp4xj2dg+Ynqsz226Tj+G96+vkqUX5imvXLvBXf/4vgbFkeQbG8vqrb/O7X3njY/t476v/1J05swoCxqOEPM9Y39jnN/+3f0ZRpDx/Zg5jDQLD4vIswgpuXv+AC8vTCFuwNyi4cRDz8rs3UV5E6PuldE4KPCXwhGY0GjHJU+5udj+2j3/88y+VxGxjkCYp5ZNSYaUldzlaRQQqJJThMULYIbQCLHmRlZZ2DMYZMlOgjnnxTpbg/RjL0Ej2amf4R1/5ysf28dILl9zhYZe1tVV63SGDwYAoKgMsWlMNkiQhSZJS5pkXZJkpzTntKer1OsYYer0u3d4hQgjq9Tp7ux2U0tRqVay1VCoVrLV883tvfWwf/9X/8AsuHidkWU6lVqWzP6R/mILTKCWp+ZLu7j5TU1N4UYXOYY8r82soAVPNJjOtFlPNOvWKz2R8hBeG5NZnY2/A+v0d7u0+IM8c1hq+9Eevf2wfP/XpF12SpgR+wKDI0UHEzvYeReGoRDW0jjl3YYntrUNsHiGsYTDsULiMyWTC4tIqTz35JMIWJKMBi3PLzM20OdjfoUgTUgSmUHQ6B3ztlZc/to9rn/qi6x50+fHPfobO/l08LVhdPUtQqXL3wQYLi2eZmmqzt73Ft7/5x8SjASoICKoNwqgGruCpJ9a4fP48zpM47Xj5298hlCGRtHiRojU9y/kz5/nbf/fvfGwf4zR11hqccyVK1zqUApwjzwu0lgglSCZjtjbvkqWCrQcdnnrmAg823+cHr3+bWx9ep9cd8t/9N3+f82uXUSpkMklKzn+WkmUpk3TC0szqx/bxX/+NVffGu3t4usLqmYiz55c5s7CEw9JPBtzc2aNVaF5Yu4QtDDv9Q/Aci7JLU8dkpkALn46GL418Lu8nLFccxVyL2cY0Z3JJRsbItfjif/L7pydd8Akv5GHk8ZCnL5Q7xrOWQB6JV7rXkBQmwzGhcIKaf4Hp9ho0fIbjIb3DTSb2Hs6MjhnRpaNMuDJHE1fC3k8qz9fEcYL0LE4E3L2/Q5ZOmG61yZKMQZ6C6VCvtdnZ7nCw32V2Zg5nNcNuzpmFiLmlKvc3NjhMJhhnsQK055OkQ4QQhLUIdYrMbW52Fq0VeZ7jSYgNvPHOBxzsHXLhzDzaA5cey6ikRxD6NKOQahiVOm8zwXYmCKcxaVYK5sIAV8JpSmei4FRQfsNacKWFvhA5uSsQFrCOCEEtCFE6wLkSSyCkwGQp1hqUKRCuwHM51hnCko//kPNHTgFGkuHjnSLr0tojCPySlqcU09Nt0jSlKAo2N7e4cuVJNjc3KYqcpeVl7t55wMFBhySJEUIQTxJmZ6YJgoDp6WmkVDgn6fcHNJtN3n7nXeZmZ6jVaif2kSQjstwAkizJUBbmmtMEMqRRr/D555+mGCc06nWU8un3xlw+dxGKIZ7K2d3c4MMbN7i5sct0fZreYEBrYYHnPvUpLpyZ5R/8n7cxhSJNT2aLDFNHmpZhvFme0+0OMHlBluQ0a020gFG3hzQF09N1UmMoRIEpCrpHG+xuH9CIGkhX0KhGLMwvsXpmBYwjT2Pu7z1AqQCpTnZCT5KE8SRme3cbKSHPLbfvPeDs2jkG/SFFcpeZap12q8nZtTW293bojQ3joWW52WC6WSNNHL1xwvXtdapRiK60qYRzrEzV+OMv/3OqzQYPbm/xt0/oI8tMmQAmBVJBYTPiyZjRaMSdu+tcuHCBNE3Z2d3i+vX3iMIWTz7xAtt7O/zgzTconCFNM6SUvPf+n9But7l1c5179+/SnmtQmIIfvPZ9Wq0W/9nf+I2P7aOIDVNRRKPRZtLbRaezjA8OSIucw9GQeGTY3drhaHNApVpFVEOWG0BVIGWIxpRpUb5DYxC+wG9UKGSVpKgiW8v4nqYmT5YNf7Q+2WAJW9LaoORIaCRIgbE5zk5wGKQE5wSucEhd48q5n6QhZ9EypJgP2Gtt8N76H1C4m1ilygBnwDpJgQGp0acs5GluqTcaOGl48O4Det0xpshYnveYmm3yg/f6pGnMoKvpHuUkE8Ggm2AyixUCm4JXg7l2g25nUuJCjUMqzTiOiaKQMApO8yXRaDTI8pw0TYlHMXe3Dnj97fdYnmtTdQXCgJYeBoMnFO1KjZZTtApNiqUmNd4xo2FYZGAzIjRaCgwW+fD6nmJxkBRYZxEIjNBITyGkQAuJdgpPhhTWkrgE62yJ5zVlEpNQDiUEolBIS5lQJChj55zFiJIMmWPJTutDgtaaXq9XJutgmUwmFEXGaDzC99dxzjE3N8twOGRqaoosLdBaEwQBSimCIEBId2x7Ls0sni5NFu3pqdIVODrZ+jwaWoyxSGkJVMDqzDw/duka0zqgEXnsPrjL5bPnmFtaKA1oi1OMJgl50kOT4ecpyjomieW93V2EhNvbH7DT6fPElUulkWaSkp+imxZ+hCscuVMYY0mThDTJ0TrAGcN4PC5jCdEEvsb3a3iehycVnd190njC3s4OJk+59vQVOp1DojBgcWmJfrdDtnGTeHKE0ic/L9V6Dd8PiapV2jNz9PojNjc2WVyxNJotWpUquILA87h8+TILKyvcf7DN9Zu3MXlKmigKL6I/jlldO4+wsLjUZnsr409ux4zdCsKmDA+3T+xj/2CfufkZ9vZ22dxeR0r3aOP01a99hR/71GdIk4Q33nyDnf1drj79KZ565jNkmeHuxgOsHbN6do3RYMg7777FBx+8x/XrNxAK/IqjVq9x5846UqgTF3KROKaDiJlWHVOkyCQlnkywQlHRIf3OEclEsZOlqIkj647Impq8qRi3Qqq1MoMgwxEJR3tpmkY9xBFgXIQ/9xTCrxKcYij8aH3CCUFFGeTqHEmWkBlDqH2k75egGJmSZxVa0RNcWfgsU9MX8CYB9QkUImC72Ob+7psMux18X+GOqWrWiPJGUmXqvT4tszP3ODwcIZWktzXC2fLv+0eHDA4PKSaKNM45yrqEYUi73mLYHzPsTli6vMDa8gJJfMRUtcGM3+comWAmhs7ePoPBgI2NBzz3zFPMn5LG/dDiPhwMWd884n/6n/8JlcjjzPI8Rs6xo15EZneJbn6X3e/fII5TZrE4sUmoAhYCyUuyQIc+fzBMmRQGaVKiiodWknE/ITXFqQt5cewqy42hGlRLxLAxpf1eCobpAcIJPOEfx6A9jAhxZUqoc1j5MJfJoR5mBK2bzAAAIABJREFU4bjySykWmtQp8lP8HksrZ8jv3cNYy1RzmoWFBYqifMns7GyilWI4HAESYwriOGF5eYkknTxySWZZSntmmo2NDWq1Gu32NM1mi7t377K5uUOz1eTixfMn9mFzixWSvLD82vwTHN7f4Y1/9gcMhjnV5jTXnpmhG1gwg5IhksQ0z16l14txWY4OQ6anNBfONVm/M2JqOmLuwnm+/uo63/zSt0jSFF2ALU7+XXqdPcIwJAg06dgwVW8wcGOU9HDGYmSdsDZPliYc9mO0ynGTPX75r/ws//l/8Fd4+Y0bvPPOu1w8v8YT58/y7vs3iJOANO3w4O5tRqOM4ahPq3Uya8WYlEqtysrZVb7zra8TRlWqtRa7+10qQcDc8iLX797h7MoZXnjueaQM2XnvLWaHW9QuLvHi538Gk3WRYZN2tcarb73G919/l7hYoHe0z+yMQGYJab9/Yh93t17jnRtD/uAPf4+5xZCtrQdYK5iZmePDW+ts7d6m1+sRVap0jvpsfX2fX/u3/j0Yaa5ee5Evf/l38bRid2+bW3dvopRgbn6WTueAxdY8129ex1lYXFw+sY8pqVloazx/gm7WqXilSze1mpsbPUZdQTzxyq/iOCczBQeHIa+JBCFGaO+In/mpn+Qvf/4vc7W6jCrATcYcTTrsHuwzjs7jnEf+I9AgH9Yny1op8nKm7RzaWYSnkULibI6THnV1hur8eRbaL1LNFqm4ZdK0w2HcoT9lud57hb3R60RyjDEKnEKJYwynMyipyxn5KaOEeDghHk4IgoC1hSW63T7DsSHu58zN1DGjPoEwRKFHtSYJQx8Kx3g4RjiDyRPS0Yhxd0A7DBEOIjySScre3h6mKCjSFFGcBu8qysw/C69+7y1Ggy5LK+ew4Tly72lGeYVof8T07gHVPKNmBI0gwNdllqUuSnfnZ6pTfHc0pCcERWGxFlTgYUxKkVusOcX0JWQJKHRQuONzBlGeXUhsmTkpJQiHoRytCFviTMsduOFhrrJEHP+doxDlmEUUltRqJqd8oTz3qc+wtdchUAodeNy5fxelyiBrW+TMzjcYT1L2Dnukk5jV5WW0FBS2nJlrLUvWtCnIshQp6wgJR90Og2GPhaUFCusYjE+m/ZkkJSlygjDk7W+8xuF2n2FsmV1cYjgYMo6rdLpH5FrR78fMzSwwXavTnzhuvXubM4sLaOlDHvPis+dQFZ+58xd59fo+6c4m1toykV4HJ/YRhcExudKysLDAaDhCq4AoiPCDkGGesbt7gPY1UVEm8pxfnGbUPeC1Vw+4/OSLrK0u0z3ssLKyxFGvh3OW8XDAzNws97aOqNVauFNof76yVEJB93AXmxckeReMIdA+g3jCi9d+hZu3bvLhzVtoETC7fIH5i5+l/v779CYRRx1YjsD4ht7hHmdXF6g0p9nYShkOUq6c+zz3b9/im1/9yol9/O7v/zaVSh3p5Rz2jvArls7+EHNgiJMhcugxSRNya5iabtGeOkOeFTinObNyjiyzTNIJFy9d5NXvf492e4o79+7i+z65ccSTjEajxUHn5BdKRUMgCzxPYqRECYVQDiklozgljiFOMpRWaCGRpnwZFs4RRCF57vjw7iaDFGpaMswLbCqIM0kuQpLCoO3xw/Mj1ie6kKdJhlYKqQSByUmlRqLxSChkyvNP/lWi5SfJJhXYHeEKSxFqtiYD7t15h25xG08UKCFBlPN2Y0AhMU5SWAMIxCkc8MlBFy01Li9oKR8jQHo+Ii0ohgOmpQArKKyAJC8T3RNL0/do16u88MxTFOMFRP8N9geWurP4xtCcmuJK+CRbW5sMe31GvZMXjKzIMLnBWnj5u6/TbjXIDAx7bUy0gD95n6nDe8zmEwLnAFlCl7TGeRKNpkqIERFnqwFxWmByWx7SRWHJazA/XGQ/rh7Cm6T08FC4h180rkyz0covuc32OMcSsMrihEQ5ibKQOYM7noELCxKHdALlykDZzEF6yv1x9ZlnmV9YYjAY4CuPg84Bd9bXWb9zhyzNKKwjzQ1S+cSTlNFoTLNZJwgjrIsZDUdkWYrne7RnZqnWaiVSWEmqtRr5IKbi+fQHJ/8u+bggniR4rYBuZ0J/aEgsGJcwM6UokphJNsXu/YSNzQ7XnpzijKixd2S5vj7kwVbB4pQjkAGtpo+sBviRT2JynCcQCRRFgadO/nS2SCpRCfDyg4i822N19QzKCbRS7H14C2cShA3+H+bePMayJDvv+0Xc/b49X+5ZmZW1L91dvW/TnIXsWbmTooaQOKRokTQM/iPQkCEBWgBBBGTYgCkbMiDBtCyS5kDk0CRo0haHwxnO3tM9vXd1de1r7svb390jwn/crJoewPOSfzV4gIcqVKFenno33omI73zn++j3+hyZq/LChz7GsLNPt9PntOVQrzeZnZ6h1+3SbrZ54803Sq0R10Wh6Ox1qYS1iXmE1ZDVlUVatRpWdpyd3pj+IMIiY7ZVYaVp8/d/7Ee4ePk2I23RVwE3sXmvcozb1/pE+Rqf/dgxSHdYPLLIaJwy37Y4uTRid8cmGo9ptaZ46kMfmpjH9Vvv4Xs1tDJU6oqw4rKwNI/vVWg0mty8sYbtuIRhQJHnzM3M4TkBeaKZn12mUZtid2+bLE8QUrKwuESUJFhSEicpg2HEcJgdaoztWOC7IIUCy8FICyk1tnRw/ZAo3kUdKGdiZAlR6gLHdkHlpFHErRvXuX33LiePTlG4Drbr4MsaFW1wXQ/XWIhD1FPfHx9oIUcDligtomyJFAJXgZQBub/AYz/yWRIZsndtm3ggGEV97nXe4b07F4mTEdXpI+SpS1rsIeQIrQ/ceFSpwHcffy9tXH5wWLkizfPShSfuY5KEih+SKUjjUm+5MdNm7thpsGxMobjx3lXGG2McIQhcB6M8XAy9eIinFZgc23MJ7SqbUpKlKUk8uXRlSUqv38dgsb3f5ezsIsOOIQ8MxopoD9dpjTbwhUAcqMdJS+DYEmGBYzk4XpPNWpVzRZc91WM/L0jjlDz0S2ErCdYhHoSubaO1QRywTb4nc3+gznbwul+oldI4jo0SGnR5Ogf9PflX7pthH2i2KNBCHPg2/uBotdpMTU2XP884aGPYf2KfbrfD/t4mO+u3WD4JtXoTaTTDXod+v0ec5kjbpT+MibOCotNhZeUo3UGfMAwRQuD4PslOl0BYNBuTtUWEcSHPifsZVlhBd3MC38Z1M+Zna+RxzuXLG9zuChQ2St/lxV9YYpTYbO2m6GLM1t0OZ442KNJ3efyF59jc7dCP+9SnAzpRD8d3CKqTtTSKQtHplO5O9VpIvVbl4fPnSKMYx3Z5+Y3XOXn8KO3pFnt7W8xPhUxPTbE8v8DW9g7CtphdmGdzc5OVY8f4wh/8IYP+AIFgfW2DI0uLOI73AFr8QbG4OE/gWgQOXDh/iit3dxhduY4tYXGuzfad6/iOx8nVVTaTgG3lMxINassfYlV0uLe+xpev3uPsyYJWakEWkKYDplo2reU5/suXvs7ph85z4aknJuZRrVaJRgrPC8nSCCioBA6jUUSn0ycIQmq1Jrs7ewwHI/zHfYSx8J0KgVPl2MpJvv7ttwlDlzRNuXt3jSzVBIGD61k0GlWSJKPbmexeb4cSaVloHPb7kiyLSuKBFTNODI2ai0aTaYXSEGUFjlaIIkflabkxF5p4PMJyXKTtoESGh0/FLnBtD4EF1t/SQu56bqlepg3GC6lpaCYOmWkx3f4h+srBcV1cYq5svMM42+PW3TcYjHNsbw7bPk7FHpPISxTZW6V/n1EUSr9PMvZwY9s8Kt130sIglEJJg+u7jPfGCFmQFIYPP/8c5596gXsbu9TCEGUkCkGlWiGJY/JRgjKGyGSl5ZVQFLrAl4IwDPA8G9c9XP0wTVPSDGbmm+QqJY59Kg2XVEVgLByrgu+MKPJSOhRZunzL3JBnGZnx6TYk0wVM2y6DQqPynCiKsFwXmYqyoE4IiXhgwyaEwDpQhtbv+7MHym6mlAq2Cgcp1QMI5f0fuTGlxbYtJcqAAnIDRkxebgKboiiQQhxg8Zp2e4bZ+QWkOEcR9UlxyIscxxKYorShK3TZ1Nzd22N3d4ft7Q329/eJkgykQ5rn5IUBKcvTvje5kGe5QueKNFPgh1TrISrPmJuuI2ROnAgKYyOkoFqvcvqJpwhaC/zwj/4Uo0HOvRuXWZ6a5thcyPX3XmM8SvnO5bfoJl0q0yGjmoOrbZxg8hfVcRz6/T6O41ANQ5xalUoQ8OSFC3iuz7dfeZUzJ1c5dXKZO7cDNu7cosgLUgTaGCzP4fW33yJNEpCSW7dusjC/QLfbZXdnh3OLC8wYwd7uZCvAZx+5QJYkeFb5fE6erLK8egzf9ZmfqvPIU4/gehVGhcPr13b50p9+C7t2ht3tLu5gDZ3vcm035+SpWa5cuYLAodGoMtwYs72zTlgNmZ+bY2t3b2IeRWHo9yMcW2DECGViLDtEF5pOp0s01iSJQUiH+Zl5jh89DgXs73aYnZ4i8Cqsra3h+Tae57O+vkm/N2R6psXCcgMtUvIiYfX4kYl57Pf6WEqhlUURtMmLjCyNKISi2VpiP06J04gkSgkqdSwXpD5wAioKfD+g0ZzCGIHt+mghMWgkgooXYtsuAov0MEj0ffGBFvJKpXKgX50TJh72oMaMfZ6nnv0Zul1D/0tfYbfe4LW3f5/uxlewvSm0/hSOVSHNMoRZZvVom8tXNyl0gucEaKORSqG/TzlsMrRilCbKMlKvzeJjpxncuYKsNvixj5xlPOrzyivf4ejSKo+de5xjRxKa001OnznBu2+8QcWF5z78DNFgwOZezpvv3SEULnU8LK2wfAvXs1Cq4DBMQxqwHYfvvP46s41p/OoJanPL7CcQCkMufLTfJI020NKQGU1hChwDRa4okox+Z4fKts8eOaHrU3cEic7I0oTqVIW6VcMkk+Uw5cGCkYjyNC4E6NLRRghJnudIIbEOrL5so2l89h+w/hd/TtDbJBc5RpQbhlIGI2W5eRhBpjWZNuTSRrrhxDyUFgjhlHKvtjo43Zc3AGFspBXi2h5+CFrlCO0TVhtoU6orrqyeOLhIlHhlnuekacJgMODSe5f4zre+zVSrRZbFE/NoeXXqnsfm9j43o5xKzeGjTz6Gp/Zp1y0eeWoetzXH7T2Lh577JMfPP8Le3hhvZolf+e/+EZdf+iJrV75Fu6IJ/fO0mhUeOn+M1+/eYWdjSJ6CqeYoOZk9EwQB8/PzRFHMaNDl7MkTVDyLS2+/Qbs9TZ52mKmdpWFJTszMMmO7vPPGm5w9f45KtcIf/u7nWd/Y5HOf+wX+w//y79jb28NzPer1OlPtdml6UG8xPz8/MY+GF7A/iNGO4J2rtziyvEitUkUbwyjOeOfGFnOzcyRunV0ChISauYneeI1msc+Tq9PMnGkhhpv0uzG271EIj1ajhu/bxInFsDdm1J38XPb3YoYDRZ4NqNZspBWwdW9Enmc0Gm2S0YBBN0XlFrI+w5OP/gieb7O4sshuZ8DDFz5EvbqAJep0Oj1GwwLfb2HLGv1dQdWZY2rBPfQgGEU2dVviS4EUCU5V4rTniETARmLTaoUsV+aRsrSL3NnbYTAssMoLLiqN2dnaYjhKMcLBsh20lrhWge94D5QUDzOEeX98oIU8z3MsyyLLc1QyotF+goUzP0tWW8ENCgbvvoS1UCeQVQapouHPsVNU8TybelBhdtbhvctf40t/+Se8+GKtpMJphaFs8Lnufd3jyQ9iGEUop8reKOY3fvYnuXvzPU6dOsvCfI1ut8/129cJ6g02u3vcXV/HvmeoVV2OHF+i5vtgh1iBxS/+2q+jCNi6fJnq0hFsz6MoYowuedCWdQjEIwSubeNYNiJziLoKmwxf2miVUhlvEMQld1dJQaE0mSrwPRdpGVwpqAuDKQyvejax0vhOqQ09zCOMKXBcCyEO0SMX5QlOv5/dIgBjDhxZwBIG7YUo16fYX6f6yEOEb71G0rmDkgILUYLjwmAoEMalEJBKc4CPy0ONNpCl3Kko/8PvwyoFRudYtoXOM2wFaRqTZzm1WskJFwcbucagtEEbAcLG9QOats2zzz3PhUcfoygKBoPJzazdzR0a01Wmmm3u3ukxW/MJGiF2PKTRqjGKB4xGGa0jP8Tc3An6vSH//t/+B8JawCd/5Hma7RmuZQVb432WG1O4xmJppsnK/Dy9S7eRrkul6RLFkwtXURRkaYoQMNVqcnR5mb3dXVaXl9Fa8cILT3Lr+lWsNGOu3Wbtzl2e/egP8ciTT/Dm66/TqlQ5+dyHOL60goPEtmyGwyFaa7Isw5EhxugHN68fFEmWURQFQlrESUKWZlRCg2uXVN9O1KdpGrQrbaacMVF/nWt/8cdcOLbCTLuObca8cOECzelpvvvKd7h4/Tq92z2kyfHDkFt3d7HcEP8QrN6SDuPRkDwvrRgROapQnDt3mn6/x3gcYYyNZXwWFxYJ/ABx4BEwHA2pN5rMzi6RpTHDwZggCOn1+hR5ges6BEGAEBZ7u7sT8zDYYIHl2lhBiHAtjBeiTUCz2iQSkizVFEnO1FSLsCK5fWeHLM/ROsPoAkc4GBVjVIK0AZ2S5jnSCDD2A1vAv2l8oIU8ihKk1AgjqNaXWTj3EczCWaJej4yCsL2AzGHtu7eZigPigSGcyxF1w9LCKTbWrvKlv/h9wlpGJgooNCBL70NjIaUohd4PKRhBcwpl1whSi8vvXuTnfupT1GpNcpGysLTKi5/4NH4lBJGzcrSNNILRYEzo2zSqIdVaDYOFcDx+45/9U7au3+LOzhrrm1coUnWguy1RhzRN9vf26PR6uFKSjnqobEjcuY6WLtbUGepJDy8blfrGCISQFIWiMGBLWWqeK3CPLPPWnStIW+K7YXlqxkGaAtctfT8nhRDigTC+EPehqfJVCt5DrjXh0ePUnnyKiy/9JdXOHnY1LJkpQiIMKFPi+OIAW1fkSKMojEBJ69DBJAQIKQ82Y0kpi142eLVWJcVQlANgg/6Al156iZ/+6Z+mKAosy/o+owZ9UJ2ktLCkASOo1Uo6aLM5NTGNcZyRbI7w/BBdwFS7zeLyIuOdCC2gHlbo7cesra9xNovZ3N3k5a98Dcu2GO9u8fM/9yLGdtC5QOPgVRrMT09Ta1WhKLAtB7uwSQ45gVZ9AzphqjXFTGuaY0dPcOnSRXr9HrVaSDMIuTlOSLKU/nhALhTPvPAsr776KlE84tjKEjPTR8hTwfTMHMMoQWWK7cE21WqFqZkZ8nSM7Uze6IUQDw4mFy5coFoNCXy/9Lx0XTxPMtq4jby3STv1mBpvY9clH3rsJK+88SqjGwPeCSo88tAj/PiLL/ATP/kZNjv7XL9xjZu373Bvq0u9VcPzJ9/Ydnd6GGOhlSKOUuqNAN/zyXNFo9FC6y2EkURJwokTJ6jX63R6YxYWFqhWqnT7XTwnZHd3B8tyqdUaJElGFKV09gc4bgzGlJPZE8KyHYwj0J7H/ImH8Gt1wkabnV7M6rnHSIAbV2+zuXYPGNCwJAZDURQkScJ4PCbLNUINcWWMNDmWSdHYKCUOrBE59Gbw/vhg6Ye6wEFhU+XC6o/TWngC4fiY3h7S9RAzC9z+5lcYv3qJRwOHt6Z3efxIjL24yN29u3zxL/8YyT5nTs6A1EjpIIWLQaCVwZCVvObJ/gWsnDnP3Y2YZx9d5fHHHmJmYYVGY4q8yLFtm0984tOkcULYqBCEcPtuh/G44KlHT1MLIAwrqKJGlhX4tTr+ubPU5gLyZI19bdja2mGu2sDyJn+8N29cJ8tzslQjigyvKEBpCqMwlkWdAgeJEQeQhxQYNHmSMFKazHMpZtvosyfYvv0O01aARznJKYWgyAuEL5CHQE33CzmAPmCmKKXQpnQ8l5aFVJpxlDN96jwVUja3N3CKFKTEUhol7hd+A1jkAozOCbAYF4LCus8zn7A+ipL6WOLj31vE929YpYOKBbqg1WrRbDbZ29tjdnaWtbU18jzn+IkTFAdfxOKA/mkOGD8qLyfqbGvyc8k06DhDaYvQt3nysQtMNeuIUcios0GzdQTLFkTjmCwaU/c8kkGXQmU48iEQoApDq9pgONymEteZbZzD99v0Bxm1qk0yttHJ5Ocy1QzIc7CM4s7tO3ylyHnyyUfxA4cvf/mL/MQnP8n+VodM5cytzPCxH/0IX/ijP+Dq1Ss8dP4hFuePUK9Pc/veNldu3WGqWqfZqLO7u0mr2SAeDTEyw3YnT7r6nkfue7hewKd/9EdRxYEBsywpqE7c5/P/429S1Ra12RNE6zc4evw82/2Y3Z0dTs+16d54g9fXr3Bl5wa/8Ov/iKcee5rHHnuKOEn44R+5wszsPFs72xPzELjYFhQS8lwxHiX4nkuvO8BxbGzLQQiP8XDEysoKxmgsS5AWOW+++Q5LywsoDXGUE0Vj0iRHCEEYVFAKHFOaVvR7k5udBoWxfXI34DtX18iEzyi+TFYYKm/d4Lf+3X/khY96mGzEX3zxd7h1+9vMF1WU1tSqi6RZxs7OHulog8tvfxPHD6jUKjj+PEFQHjJK4sDENL4vPuBmp0FqH8+eY5EV4kFKpZritOeJPYfty29y/eWvsuyAyvo8JHwecSTZ7Tu8/J0vw+gKpx6bohEI3NgD28HYDqlSaJGgdU6hcsQhzT2EQUjB3GyTT336KWwrRABFnoIuWFmeRiuF7Up29rf4sz/7f9npp8xPSU4fbeBYYxxPUA9A0+C7r73MzVvvcXSxxomT5/n2N79JlGUEjckjtnmWYNs2M+0pimhM1bZwpAGZo8wejdzCUhItDEiwpI2xyp19KGzeS8Zksy1WlmdLA2RjiPOsbEBqhUpzTIVD+ajiez5V3+fiI4TEssomjWU027evkV18m2qrxf5wiGnUkZaDnccUotw8BICRaHngKK5sdrWgm2kqhyzMLMse/Fwpy8aqbdsPCrsQBw5Fpiz2H/3Yx/jCH/4hTz/9NPfu3WN2dpadnR2CaqWkSz5o1sqDIafyhnEYr94JKwx7Q/Q44fjqEieOLWOKAkfa9JOUWHksHDvDzTdu8/Ir3yKsVJida4FV0GpXeO21V7G0JPSrjHp3GHY7zFPh+ec/zsuvXKLS1tiWy2A0mQZZqbUocsXO9h6f+fiL5GnM7TvXCAKXra019ro7bG6vgyz4xOKH2d7aQQhJvd7izNnzBEGL7Z0hF69ewg9DpqdnmG63CCsermuzvn2XPE+JdiffDBACy7IfTNBqx8KS8sEpfef2ZTZuvUdrepr1jsXlrbs88vgZdu/doV1p0Bum3OrtUA8CHmpN8/UvfY2f+aX/Csu2qQVNPv7hBfAE6UvDyesjNQjhYFugtGY8jnAcSZ4WdHr7TLVmiaOEer3JqVOngLK/UglDFpcWcRyLPFVkmcKSDlKWRsdl8R6iaxz08CbXjyRLiXoJRZTx0o0eeC2UkPT6Qyqezde/+Rrt5jT7u3dw/ABjabRJSbMEPUqwbYdWK0BnQ7770pdJshzLc5hZfJxnn/s4tVrrAQPsbxofaCHP4hanVz/BYw9/lvzaBpXsHjNvXWEntsgfOc3/83u/TX1/DdXfIZY5P2IHiOtvU80M//zRC+yshWwMtlkPCi6FmoHew9Yujg6IC0mtcZwgzOkPNyfmMTtT5/kXnqU/iskTl1F8A60ilC4besrk5QhtCl/+ytcwTp2k2OTLX/8r2j/1FH7YxBdLWLTp97dBFijpsdPpM040Tzz9Idbu3cQ5hLUSuKU3X7ezi9QJeSbJjUFJ8Ishi+SMdMkYsQ8olam06Xgut4RmvTrFzf1tbrzybVpOgNKaqIjKBSCgUAVRlBIeYoaY52WzUsADiMIYQ6EKsiwrfTyloCoi3vz8/0rzqR/mk7/yGyg14NqNK2TXL4LjYIxEIJBC4wrIUxv32Rf5t7/5W/SHEZ//j//n5AViBFmeY8mS3iWFIMvyA79LgyUtEOWtYWN9jevXrnHy5En+6Av/mb29fZRWSGnxuV/8JU6fOY0lJIWWFEVJhxTSfSDpMCmSXFBtzKCimBeffpjFiiHubWJLzZHjpzn/qV9FVpo8+emD989zPvyRD2E7pQG4KTSyUPQ3rnDr5SHtpdOEMyt87MwCzZkm/8ef/RtQkkdnJ7MjrlxfwwH2trfYWL/JcNTn9u27nDl9lo9+9EUa7Wk+/qOf4fTp42xu3uX3f+/z/Le/8Y8ZDAY4XsDadoz0AoxUHDtyjMCxqVQqVGs+tm2xtn0LgUs8nkyTXZ5ucK27TeiHZElGEed4QYiywRIpX/ryn7O3vU9N1PnmpW/QTfcw2zd5tC6ZXjrBW9ub3Op1GUQRq0XCP//X/5K+LfnlX/01Uq0QBfiew/Ub13jm+Wd+8HNJNEnSw3UdjCmwLJc8MzQbIUsLNW7evMv83AqfePHTzM7OsbOzS1hr0un2CIKAKB6jtUQrgdIFthYMhh0cx0Vr6HYHCCEOZZslgDY2nhvwi586y9gWvDcccuuy5rkLz/E//0//mA9/7DN4fo1zq3NMOyeotO6Q6JRhrnGqMxhcLF0QTscM+h3yLMcLNUrGFCYEIw/zgP6++EBlbCvBLE8//SJT7WXk0hzDRDHUOSrZJ1q7QTgzi3v0BOLYWTr+FLd2xjSOrxAHHvfqs1SOPc7Z4CQXrgoW9gtmchtPe1iixdL8Uzz39N/l0QufZGrq6MQ8hv0htmVz5b3bXL6yBUKidEyaJaR5itYGow0qS1HK4Fcq5GnKqD8iiQW+t0SStrh1q6BWn6fICzbX7iCEZn5xgb/7859jZnaeKI4m5uE6DkWekSYJhS5ItGasBQPlkGsPsgSjcoQ2kCvIFXEh2NYFwbFFnnj2CZbmptlYu4W2BLkQaAFIgSUtLMshz7/nJ/iDQpsCrTK0ylEHk5FKFeQ6L0+yQqPygrwQpLng9bdeZ/vMZimjAAAgAElEQVTuO4h8ALUmmZQljm8EwnLKEzSCTSNZUw79QtIZZ+zvT24iaQ1aGbQuWTlKGVRxcJLWJSOmKDS5hsXlo7zwQx/mG1//KkHg4jiCjfV7zEy3mZ6eocgUWVqQZcVB8/Pg/Q9ekyIfK6JhSholnD62QjYeoPMDOMHxMV61bASnESZPcG2LsF7Fdj1ybYiNJrV96kfOMnvqOZyZo2i/ihYOJ04+hCosxknM9JHpiXn0hxF7ex2kgYcfvsBgMCZPNZsb+3zj66+wvHyCfj/h+vV7zM0f4Rc/98v0+wNq1RpSOOzvDLh17TbTzTrHFlcIg4CiyEtoUCkW5o8w3T6Ca0+Wkpifn8axJa5jgylF5wwagaHieVy/eoNU2mwMB2xu71L1AmKt2On3ube3z+XbG9zbHxHj8tbl6xw7eZpHH30Cy7IRRjzYWAeHDGqlaYrj2EgJiPKkLaWF63okaYrShl6vz9LSMtVajdZUC1UokiRFK02apiXLTQriOGIw7GJQWLbAcWza7SmCwOcw++W5dpXqVEjiaHbCgNuZxa3ru3hpwOL0CWrVOZqNeaam5rGCGkNdTiePdIHwXBpehZr0cf0pjFUjqM5juy0kPoET4loBrvQJ/cmQ1/vjAz2RJ+mAYbzL6gmH/ngJTzbZzDbB2eTWjcuYRgvVamGxAtNLXNq8i/edt2gfO0p1ukERtKgtt7H/sMuj7+1xZzVkY3mR+vGnOX7iOfLYRxfgWJO1IzzH51vfeInvvnGF4yeP8Cu//EmiVAEOGFFOixqJUQY/CAgCD5RiYeYorfpxXn1thy996Y949+I1/uv/5h+ytLSCK78NRtGamkIIi0pY4TA6QDktCaQphTDkuSIzGmk7hK5PlJRc6kIYTKHIMfSrDsW0x/SxabIiQWvFOI2xrUqpbVLyRx4M4xgtDvOSRZmS6lciLKKUqtUlh0Va5Yl2SxreGg55tz8iDzNe/cZfs7h8hLxaxQ7qBGmMlAIlcmLLYdsoXo56zMcRRkh6vR67G7cm5pHn+QNoR2tdMiXeN5D0gM8uIM8yomEf37NYmJ+lGvrkacxHP/ZRKvU2iSrK07fRCGEOrsz6AUV1YmQGZIGtNe12g+5WlyLLkVIwTsaYAzNpqQuMgdwIHCEwBwwfaUmMFBhZYebMcxRCYIQPhcB3azRqbaQDUZ5MTOPoylE2797CdSzefeddjJasrp7ixo079AcDNrc6fOELf8qZcydZOvJ3uPDo43zly39NNq348EdeZGfb8O7eNraARrOGMhm2I5mdnWVjYw3PrZDEOfVae2IeU40q061GeTMrFFIYpLAwqiBwfS6+cwlfCKI0xnV9js/O8vLlqyx6NkfaLYZaoCyHcQ6ZdPm1X/4VHn/iKYqseMCYQsNgMBlaKU/KiixPMKZgNB7RbNQYDIYkSUK1UiMeJ8zPzaOVJs9KxchoHJGmxQFmLtG6YH5hlna7TrdbDpVp7dBuT9Ht7pfmzBNieraOl8Fc0GRq5ijHFis8uXCOtgo59fSzfOwzn6Jan8ZyHTa3bvLym+9ydrlCXwpsHZBvjfFEhchO2VzfQhpFliZkaotuZ4Ou6pBlBmHZPPT40xNzuR8f7Ih+tsNXv/47RFGPpeWfocgC5PQcN7dus729g67WIQipOB6t2aO83Nvg8ntvs3p3jeW1u8w+fJzzp0+jghYLV3pYs0c5+uTPEZw+jy087t7qkaWGLJ2scdJuzPPam6+DdLh49TLdwYfp7PRKLNiS+H6AZ3l4niH0IBvu88Mf+QizLY/P/8Gf89WvvsTtu2s4nss//Rf/iicfPcexo3PUG3NcvXqNWjDm7vom1drkHbUoFLZlIynZGsoUaAzaWCR5Qao1ns5QhcPAhp4nSCoWyydXcCsevd0R+4MBmTK4JSMKIb/H3tAHE5aHgQnSstBKPxjJB0AIHGGhLMhzwbu64G0NUVilWqnT7YwZxzc5fuwIrdlF1J0bFLYh8SxUZYZBFDPIB1RHERpIRj1kMfmLWhRFKcB1UHTLNMQBc+V7hVxrTXd/l821G8xMNaiGPp4zQ2d3m/V7d2nOrBBWwpKOivi+zeFvEiaLEI6gUbWBHMsC4dgMB2MGo4g0GhJU6xTCQQu77K9kJVvKMhZSa3AMCI1Vny6hIiERqsDG4BuPSGn0IeyI0NNIMsJKwMbGOr1elyQvmJqfYunkCrvdfVqzLdJC0RsmvH7xLe5t9ljf7nL+wpM8/vhZHDvnypVLYEtWV46ysbGOUBZCO2RxSqViMzU9+Wbg2IblIwt0hzmO7WJkge1ILCAZDdnZ2iGoV3jk8SeYO3GKmzfvcC3KCMM6bSfEuBGGIbZj05hb5LGnn8eyLYpMHSxaQZ4rOp3OIetDUeQJrmeTF5pqtYY2gp3dfRYX5olGXaanp1k9tso4ishzzXA0Js1KSutwOCCO4wNc3NBstun3B0jpMD1dQjFRFNNuT97YWnMz2LsRDe3hXdsiVYZ2pYpT9BF796jMP0XeH1BpN3G05o3X3uHSRYuBzgBJ0dP4wqOwcwa9PkKD5woevdClEhqK1GE8Sogywc/+vX84MZf78YEWcktm9PtX+cpf/Q7PvuDR8pbYvdNnb6eDLjRetY7thTSCgEFnnff6d1g4YzPqjLh9+SL+rau813qFxV7K3CjDl23mTz2HPdsk7m8hHUWhY6Jo8hVtZnoWKQ0zi/MMhin7+2O2NjtEcXQg5wquZbGy2mZ1eY7NjZTO3jZ//mdfZW/nHpWgyvT8NKM0Z2N3h2tfuMbjj57n5MnT9AcbXN5f597GDouzk3mx4/EYrUpMzpKSWMVgW+R2gzicYX+4Q6MoKLTHLRWzI+CZx08zs9AmylP6ccrtzT1SlRPUwhJHtqwHzcFU5VAUD9gbPyikZZUncSUebAJCSCwEBQVdY7GubZzQ5fTcHKHnk5LRCEJqzTqO7ZEYTeIGiFOPMXPqEfoXrzCzo6iEdbSBdDzANZNPoElS/r08aKQZU7Jg7v/+Pi1xPByxfvcu0ahHkcVEQ8nUVIunn3icazfv8oXP/w4f/ehHWVhcIqi2sB3nwb+/v1FM/DyMZHFuFkzEIB0jfBdHGqw4RhUR61ff4OSjz1OSQku5XGNKDF8cwA/I8hclLIwSINUD9k2vN2KYRDScyTfHVj1kvxry6COP4NguoyRjc2uDx594jMWlBa5eeYX2lI3rFrz+2jdBwp/+yVeZn5/lueefZWVuieXFGXz3IdIkYXZmBlXkLC0u4dgOu/tbVBohQTi5KX/x4juMhxFxLtDXLmHZGtt2CDyfezevYts2juvx+NNPsru2SXdrm0//+E/wuZ/+Wf6v3/73HK1V8dtzqAwuPPEUU1NTGA22pBTNk4Ysi9nf25mYhyoMQrhI4RF4IUa7OKGDm+UoI9EaTp44xvz8DEms6PXHDEclvFmthVhSMRj0SrhRC958/RKFKrAtl143YjTIEMIlTQ7povh1Kr6kPbYZvXEZZXI6KiZKUvKqZvnkeRqOjx31CVXGueUlXrt4AwEkSYF0a0S2IB4NqXg+wmg+8eILXHjkKEtL87h2HdvywapPzuN98YEWcqVsMDZpPuCrX/7fqQerOHoeoSugU0I7QDo+yhF8652vQb3HyiM13LRC3hd0uyNe2rpFM9WcwmGeESJNqcWabjcijvqlJschNKalo3N85Ief5eKtbTqdIX/1V9/i7q3LFJZHGIbMtKdYnm1gO03a7WnqtT1+89/8a5aPtjh37hi2U+WdS+ssLB1hMIhorzY5fuIEf/3Vb6DNCMfxSbICKSc3TYqiwJIOvu9jWZK0UCjLp3DnGDQf4XpnQK24Az5cLwqml5ZYPL1M0u+ilWGzM2Brf0jgW2RBUSqtHVDClFIUeYERmrw4RLNByFLRUEjut00O5oGwhcXNomAv1VQCj2i/g6lWWV5dBEuiUsGOcbFWz1E7foLjn/p5gpUlFp7Zoet+nnt5ilCQj8e4h+heF0VRNnYtq5RyOCjkSqkHDBbQbG1s0tnfJ097WCZjNChAK2ZnZ/Edi97OPZL+LknVo9Mb0p6ZIwxDCqUwWh8+aGEs+v0RWT5ie6/HkbkppOPgxhHVtIpKh2RJjBfWSqz8PiglxIGhB3AwbWpZDqrUYMMIQ5pl3Lm9jpYR/e5kmVLX86g36ky1Wly/vU5hJCdPHGduqs5gdw2IyMf7RD1Fd3udTNugc9bu3eGvvvglPvT4YxxZXOD06hEuvvsuq0ePEAQuo9GY+flZtDYMhwOG48k3pS/+ly+ioxGZLfDE1wgcB79SYZymfPvl71KrhbQrIVO2xeyJZc7OtxgWimMzVSoVh9nmLO7egLSAX//VX8LXEdHuvRIiKzSBb7Gzu0Ux6k3MQ6lyLmA8SqhUqwhc6tVWKYa1vcWFhy/wyMOP0N3fI0sl333lDcKgzurqUY6vHmVn+y79/uBAIbWEEZNYYTsSmaXYjo/WGveQCeQ337vFUlinqgxWLSBJNb5lE4SSqu/SrHmQpbz65S9hObDx9k3MTsSxRoDjV7iRJoyMoRba/PCHnyIZD5lrV0lGCc3KNNXKDLbloa2/pRi5LQOK3AEi5uoLTE+fYXffI8p8qlNVwkobK0j547/6T6TmBo89vwLeJqqWY81a1LWkqqbRieRWx3AjuIe/d4cpZRNYTX73d3+Dvb0b1OqTQeFbGyOUqLKyUue73/nPxN0ejzx0lqot0TH81n//28zMTXP23ApJOqI/GPD00w+xsjKP7VmsbfZotmf51Gd+jPfevczm5javfvdVXMfh5KmjbG7dot1e4PlnPjwxjyROMCYlzxVJPCayp2kc+zh240mUvczWY6tcu/PnrC4OeXKmzvx0HbeIiUzKneGYK7fXKBTEqWYQjagFIfZ9SdL7kIop8eTJYVDC4GgwUpNrQ640WZHhFpp3lUIEdWSrUQ4i+VVGw5TV8+cYmwbP/rP/gdrUHHlRFl8bibs6wz/4l7/JYGMLNdhFd+6x0JyZmEWc5iU2f3AKF0Jg2/aDk6zSBXmWsrFxnWF/FylSjMkJwwZXb1yl09nhzOkztKcbbKzfJMsGOGGFjbWrHF19iGpjFq2y7xsc+v8LpRRaSWqVGV762mU++3M/hh+AvVhBeAHN6XlSLVCZwnNs5Pum8MrN52AUWymkEtjCPtAfE3zna99g4+IWju8SVifnsbGxznA45N1L7xLHGc1GHZuCnc17tBp1mrVTzDXO0u0McD2P3f4+taDBpXev83//8Z9z9+olPvT8M1SrNfI85/d+/z9hTEnzHA4GWK7PbmcXJ5i8wf7kR58nH3aJyMmGGePRgLWNDdRgxKeePc/MzDxFnlCzhkTRiCzNaYVNrr/+LZ5/9DTfevk1GkGTUTLk6lf+CG3Mg/6HwSIZDxhHQ55dnnyD/e3/7fewrZIymKYZYRhiC4s0SajVQv7Vv/gn3Lt+j9vXbhFWahxdnOPMmUcYj3tE/X2OLi3x+7/7BYIwJIoitDYoVZSsFVWuufuHiUnRbKxye3Odgau5uLaB8FxWZyscX5njW1dusv6Nv2bn5nW+8id/zMPnz/DMuVUWn3SoDxVZUfDK+h3eGaVE1Tq1aoBvFezvrBFHS4win2ptCoWF0n9LRbMMKZajMNisrv4QljXLKIoxlsHBo1qv8fall+kNrnH24TqeC5ZxKHINOi2nurRBaQuvbTPIemzfvUq9soQJQ5pThnqjjudNXpjDqGSj3Lt3j+FwgFyYZ2tniyTqkSY5cRwzimPuru3huJIsE6ytdYjigrzI6fRjTp57gv1uD6XUAR0KoijmjTcuMRqPWJhfQenJk0naiFLjHIGyfHSwiqycQ4bLVL0qyeZNVo61WahLZJbQqFaxhUELh5tbXdY29rFFSWHMCkOWKzyr5FgLKXAsq5QxOMTAINM5qVGY3BAbg9Y5BoNjGzw/ZBznTDUbpbbGcESj3qLTH3Dn9j2eeO4UQbVFmmQIBFpCLgxSl9hCbXGRW1dfpdvfAjEZ4iny/AE+fr8wZgfj4UmS4PsB/V6H9fV7jPq7VDybEydPUas1sTHoImI87jLojSnynHgc4dgOtpaoZICpNCjUIdNigBf6ICyUhp3tLvFwjBdU8StT1DEI28VxPBAHkI+0eKAWeVAGHkBByhzceEBHA6699Qb5sMA2HvEhzc5qtYrQUKvVOXdiimPHjvL6m2+wcGSJQuXkKqXT6dOoT5EkGXs7e5w6vUKRwu1bG6xv7/LX33qJ9lQb13WohAEz0zM4rk2tXiXOyluOfcgFpd/pUox6JJZib32P5lSN1dVlms0pLMvFdnx63X0sqyCoVVF1D9fxECrF8m0sldLf3y6VAIf9g6Esi1EUgXAIXBs8H+sQcoA4qAUycHEciyIrp8VbzTp5kjHqj/nOzZfpdno89tjjfOLjn2JzfR1LQjQaIixBtdokTkp3Js/zyPNysNC2LKIoolKpPJhn+EHx4z/+d3jj5VfYW1vj9s5b1MMKMs55+btXaByd5i/evc7plXmU73Fzd0Cnl3B1vEc1N9TDGvXZOfx8n7vdMVeur5EMehxbmWPmSAsrCIiKFIygMJO/L++PD9YhiHLhVipLNKaOkaYOi0sN0ixmNBozSoa8/fa3aU2lNFopWo1xhEIbMNpBYuEIG8/3EXqIJRW3L7/J0vJjVAIP143xfIHKJn8AxipwQkG9GXD61EkQivXtdYZRn/Eoxa0FRGnE5rY6OBlCpVpheyfC8z2krOH7NYIgIEsTWtU61Vq5y2/c2UQKm8GgyzsX35qYx2ic4jo+KkuptRYonIfw/Dlcz0ZkN6nErzE926S/s8XSdBsbwXgwoh9rbq536UYJtnXQbCs02cFk5P3GoGPZJUf8kEI+VgmJUhhlyC0b6ZSFR1mSgR9gVRxcv8L6+l2MMWRpiucHHD35EKcffgwtLR6I3ArgQPOkbDRqWrNL1I6cpLO99TdaJ/fzh7IgFkXBeDym1+sRRaX058z8EqHrsLJ6jk63z87eAN/VqN0u65sdlpaOECU5jYZFu9XElpBlCeoQSV+Ao8dPk+c5YWhjiRHjqEvFhFRqC0xV24igiiXsslib+5Oj9/P9XlPVGEOhc4TtYqPJhpvk0R7SSHRSYB0yGj8aJ2CgUIrQc7h17QpFmuDZDp39fVZXFkmyiHrLo3t7lySJObK4xLifUvFCcpGC0OVksCk9ZZVSFIViNBpTbTQIcx8tJkM82li4XgXjGGYWXITUpFnKcBzjuxDgkUQZtkixHYmxA6LhiHS0RxgGHFte4L2bG3jVKv2k3JT39zukaYrrVfEdG9sW5GLyjtKemiFJEmrVBjs7O7iOR54lqAKyVLG6eoJKWGFvt8Pmxia3bt0mDBq4rkWSjjECpmYlhTLEcYIxkizJ8dwAI0zJS881xSHfF8ubYmHlFDu7+/gzIZZX59yFM1Tv3EAHHtffvkG7XiFotfnuOzcppEDbOXKgUNtjzrZWMdMNnl68wJnVZU4sH+H40aNUWrME1Tq2DEr2nPxbKpolpcBoG9+rMrO0xN3be1RqPn5h4YUB3/jaVxkMb3LmvItNglEpSthgLISwEMYCI5FYSHxCx6LTucZ7b32Z/ZmV0jNRFhzS28MOAzzH4djxKkvzs2RpQq5y0jwlihLW7m0yGvQZDscMBkPiOGKv1zkYH7dRhUDal3jmmWfI0pjq0gJuJcAOfBqjCCkscpPx9pX3JuZh2Q5CQqYyji4u0L23y3jvHdKBpmlt8vCpFvv9ffzQp9FoIDWMY8XGVpe769sYC7QwSEM5sXjgMP7gmp+Zv1FzL841QwTVoEomUgq3hlVt4bUXqS8fI/3TPyFLIpI4ptlqUalUSJOcx595jlp7hlQppBQISgmBA9WW0oACQ1Bf4NwznyIbTB59hu8V8e81NwXVapVKpaRXaqM5dvoYnuOUp9VqnURu0Zo/RrNeoVar0VpKWVpaYtDv46AIwpDBOCawUwzy0IEPg4tXdbFDQ63WIpcGy6nihHNUQg9tlbLJpdm3RB+4ud//rO9j++V7FUCK0jlax/iBJAxD0jQlPkw0SwviKCaOY1bn23S6PUI/pL/fYXttg9WlI8SjCJqaPEuYnZ1FF4apRhXfEQwzG6U0jWaD4WDAaBRhjMBzPYTlgKWo1Wv0DxER29vfJ9rfwq65hHapbpikKb2kixSSpaUj7O1tIShI0xTbC5men+fOzj6+7zNzZIX9qCDNM77z+lukhaE3jFjf2iY3FlrlWJYkFzb/ZEIeJRSiGQ5HBEFINI6oVGsMhiNc1+Lv/f1fxLYFSRyhjaIoNMNRRL/fpVLzqNVq9PoDWq0pqtXZ8pDWbDIej0nTFCEEw+GwXGuTnotVx2vOsTfOWDxznOnpBV65eJFsNMKvVrG9Cu9cu8fx4/Pkgc12f0xiQcNzabYrpLUWP/vZz3D+7MMsTy/hYIM2RHmGlDaWXRbyQ01/3xcfsPkyOJZPEv9/7L1ZrKXXdef323t/45nPnW/NxRpYZJFFiqIk2pLlbsmDbDmQFTfcRgJ0kAaSIA95CIIgQPIQBGggQYAgL0En3Ugaie3uTsPo7rQUx7AUyZYpydZAUSqONVfduvO9Zz7nG/eQh+9UkbKb97KBgJCBWsB9KYJV657vO2uvvdZ/SLj63BniqMHDBz3y3NDtLvP6j7/G6rqkXvORVuMFAc56SF2JyHh+gC4cnlCU1scXCi+Y8oPvfZX+YcqzHwspdVmNYo6IWq1NURSUZUnUCdCm/lgDGwfnz52j1BpdapIkIUkSdnZ3mUwmjEYTJuMZ0+mAf/7P/k+MKdk72GU8jVFSETciLl68yIXz67RbRy9NRpMptai65npiH1tsgRxRDxXnT0Scv3SKxdki40GfKI6RDnKt2Nwb0Z/MqmJiKxanVJVRB7wntvNIu+RYWU4Vs10a/LBL+6l1lk4/Tef0FZbOXUJ7Pr1/+A/IpxMazRYnTpygVq+Tz7bxIw9kpbWMczjBY4y2s++pKZZGENaXeeEzXzwyD20qwbHHAlhzbLu1FuVVtoDSUzgbYYUgkAGpszSXVnhhcbVyJhKSJQWe8vBqXaTOq4MtLjFGEPjB8ctOL6/UJj1BvdMhjmIoLUWpafgdlCfmnqXMP//3xil/+bOWotpVvAenrEYlxg4o8qOv8FobPN+n22pw4+4DWo0GyTRnc3/AzvYurdYd9vb2aDQWOHP6AmluGY7GLC0vcu/uiG67i5k//9k0pdvtVqifJCGKYg56PcIwOJaS/vDhA/xiRihipkkPPwgpygJjDLVajYP9bcbTAZ5XafAbk7O1tcm9jS2ajSYr66f5uVc+xVtvvUkYJCSFIW62EEFIqi3pbITWBnOM8Yhz7+cWVLfkeq0BNCjznLFOqNXrZLmm3z9gdWWdVrvG2smTCGmI44jRJKEsS06cOMFoNMI5S6fTotcb0W63sdbRah2NFhknhuGs4PTlK3z7zde5t3eAKjSTccJSENGbTMAZ+mnOzEC00OGLn/8Vnrt0mVNr67QWurQ6LTwpQUaUZcX3UJ6P8hU4b+56++GpnR9pIY/CEGNnaJHz9/6rX2X9xCucPPNZ4uZTvPbDH7K4tMvFC9WszGmP0mUI58B5GMBoA1IwzSc4W+mJFNJRX4b2WsismIDz4RjZ1nYYI+MGQRggYx9dlo+/hGVZzcjLskRrjZkXl0uXL8+LoqmkWl3ltflrX/zc487xEd5ZKYVw9lhCkHHwzPNXSWd99I3bvHSty1OXzpKnY3a393n33YR2s0m32yWdzcBzfPfuFl9/7W0yDZ708H3wPUW9GSK0mV+d9WP7NjcX0Doqln/9t/nha29y9fO/yue+9G/z5hvX+fqffYu3/snvcri3xeXzT4GCOKrxyReeYWFxhY17ipvvfI9PLK3jqejxKyfn7NLKba8qYJIUa0A0jzZ0SPJyXvA0vu8//lwzXWBs9lO/h5SSTFRuU/CetMCjEOLRM1W40s33K5ZCH2c4B8997gzKc8ySKW1/HaOaqHLM4P73yNJLrDx1pTooCBFWoDEY874Z+fwA0lojVYznFNJlzIqc4d6M8eE+tiwIxdGF6969e9Qjn3w2Ic1KvGDIaDjk4sVLvPjKZ9m8fZco7vDqd19jeXkZ7SyHu5s8+8xlmo0mWaZxCNrtNo1GWhVLY2i32/T7fSa5JisqjZCj4vTp0xxu3GE2S4icR1kWLC8vk6Zp1cG2mizNsehBEBAEAb70OLe2hjGWdDTkYNDn3MoSaScliCLCKCbNcnr9AZ1aSFmWjNOjRzzT2Zi8yLHOp9/vEwQBjU6DdqtFVI/IehaZGTpLa1jPp7CCQX+MlFBrhOQmpd/rsbC4QJ6ncyhiJa1dloayDMnzhOn06B1bs1kjiE5R69T5xcMd3nzjbQ4Ppti64n5vxurlq5xaW+ITL17jFz//S9Q7y8jCYI3BFwphHU44XCDRWlA+0v+RYt6IC8qiEq37sPHR4sg9H4nFOQlywo13v8OPr9+muXCBW+/eZH0loJhmGGtwTR8vsvieqsyBhcAYjbUlnq9w2mIsOGFRAWgzwzlRFYxjbiTTJKnwq7MpYSNCKYXv+49fwjiuLLgeFcU0y8iybG5KXI0OHtHGw7mOyfuFnZibLBy3/u4dHvLuW29Rb/j4XkxRGu7duM1Cq86p1TUGO7vUpEboEqQkA16/cZdRkiN8iRIQBIqw5hFGPjqtNEmqw0QSzkcI9pgD5RNf+jv0oq/x9T/9Fv3ZlG+/+mfs7+6ytrbIJ3/hc6RZwf07NwnrXbb3e0xyS72zytbmJk8PB7QWT/B+g7jKoKKiIVXMPb/qzo/RiS/fd6A+Wnq+dyBZpFQ/1fGqOUzx0eH5/vjp//en/87jOvLJdJdWq0W73UAPYDQtWOvWCGSGm+0j0nMYr47yQ4QHwuXviRy59/69x4c7AuEE+3s97tx6QBv1/ioAACAASURBVCg9anUfa47OQ2tNHLcxRhP4HoHv0TyxTpln3LrxLrEfUG/WCcchncUupbXs7T6k0BrP95EixvMCmo1WtYh2BUmSEIZhVbyMAAXyGBNoM3dtajYb+FoQxTWsrez+VpaXmcxmlNYQxzFRFBGGIWEQMB6PMRj8uJJtqDXq+CZCFyW6yGnEEXJpiUCKCi3iHz0TtRZmswTP87hw4SLGGHr9AyZMGJZD1tfXOdw/YGu7T6vdmsN6MxaXFlBKsLOzTaE1xrmKnYtAKg/leSgVkmUlo/GEyfToEaDRKUo4Wo0mX/yVL/DLn/0b5CZmmpZok9M+eY5W6NMOK6cfowVGVnaHGofEIpA4IypbRGnQxqAe397mfy5+Rjtyo03FzDMOMARRQZJvsHn/FmWREqoms0FO7DvKUINn8ZVACFV1E3O6tZCgXYkTIaAQ0oKrZuPOSYw9+mTXzqLmmuV5Xl29i6J6yYHH3eCjAu95HnEUV4u+MqMsC8rS4pygLKt/S0r5eMxBJX99rHysxDEeDHC2RrNRvUjOCcaDIUGjQaMeE0cKnGVSFGTG48H2IZXBtAElUaEkaIgKBldapOWxumMYBDgH6THL3yJs8covfJbZbMi3Xv0WL338Zc5fuMipM2dodZbY2d1le/MhtbiGNSlxrYE0OTduv1uJatkSnKlw6AKEq4gy2DkO3Iq5r+rR74fWBqUq5cNHIyFjzE8tPh/7gr4PXw68V0j56QL/l/cDj5anR8Xe9g5KCFaXVnGqSy9L6BtF2yux43084VEJMluccD99YItqO/DoIHmkLFkZFgwZj1Ji5SM8yUwf/Z62Wg3a7SbT8YjFdod2q/0YwZPnJbNkQponTGdT7ty7TZKVXLx4ET/wSNIUo0vyXNMfDFldWSLPNI1GA2PMfLSoMQ7yY96P/X6fRqOJij08I9DWEchK5156Ps12QBCGKM/D9z18PyDJUvAeud9YlJRk2oEXkTtLGMbk1qKFhxeEOGXRxzg3CSFYXl6h1+szmUwrKGIQE3oBTjl2trdxzlJv1GnUG+RFThT7DAY9pJR0Ol3CKGA6nYIQdBcXybOMPM/pHQ7odDqsrq4zmRxNKDTaonXVLZc6oL24TFTrVGbJwpEZhXKu0lFK5wtnNcekegojBBKJFD5CODwUQljkI0cua6pRqfzw8EPxbyKV+CSexJN4Ek/iZy8+UvXDJ/EknsSTeBL//8eTQv4knsSTeBJ/zeNJIX8ST+JJPIm/5vGkkD+JJ/EknsRf8/hIUSt/+hffc5m2lAasUI838ODwfMlkMpmbC3hobbDWPkYmPEIfSCnIspTZbFbB1WwwR0O4uS2YwFr4h//9f/qBIMT/8l/ddUiJrwR1CTiPQkDhHEooah540hArR913KFvwh1/9PX783a/RDgwUJePhEOcKTFlBq5rNiEYU04hqrKyszBETjv/1H//zD8zjv/0f/j03mfZ5+umn+dNv/JD19RMsLndotTsYNO16yebdPWbTkqevXiFu+dy5eYtiaunvDfAizcJqzMJCg/FQ8Yuf+U0e3Nrizrtv0e60+MGb17FSoryQf/A//8sPzONLv/lx19s7JBmUPPX0aW5fv8vTr6yztNDmzW+8TW9zjKcdylnwAgoXYJ1GBT5hM8IgMLnGFJbpLKkch6wh6nZoLl2gf2sTIwbgSjbdB7NPfuu3ftNJLI1aTGepwvpuPtxjOEiQUnLy5Aqer8jyHGcsSgicrt6P0lmyIsfaCrlU5DnKU5V7eVlBVbudJmmWUavFfOUrX/nAPP7rv/efu1/7pV/A9wTTZEqep5RlweHhAetrawSBx6mTp5jOpmxsbNBqtmg02+RFwe7uPoPBkDTNOHNyhbhe59W/+Alf+6NvM5mlTPMZYQVEorCOjZtbH5jH73zxJZcUGiE82qEiLzMKK0EInFQ0MZxdkCxGjlGSE3ghjdiy09c86KVEfoQtJjx3ZQ1dFmztZ/gIlpYitns5hTEcjgssgv/r1XsfmMcf/f0/cSOdMtQZy+fO0l1ewQqfWWrR2qKdh5QOKS0BGjubcXox5js/+gHv9EZcfOopPnHlLLXY52tf+yGtZp3R7gbaliSzHCEl/emMj3/yBf7Dv/sbH5jHF/7mK+7v/Dt/i62dLc5fucqr33iVX/i5l4ibbQIpMUhQEW//+Lt889Vv80u/+st4umBjt8eD7U1WVtvoIufKxavEMkabnEw0sHnBt7/9Z1y6ssTK0goPNof8T//L731gHsYY55x7DG/9K/E+COojxzaFQLiKO5IJ0MLhAwYocRgqaKJ7HysaB6c8/0PROz/SQl7ZjoGeO8WXZUmWZRVja1JQPjYW4DG851ERN8Y8hqRlWT4n7JiKUWgrM1ZjLO5DWHkt1iqst68EkbNYB5m15FQ+xYs1h00niCxFT1P297eZ7dxktSFYareYTWa0aivEgU+jVlHWwzBAWBBWYJ0lSzOS6fTIPE6uXKB56RrWas6eOkdS9HBewU7vgOlU0I2Xuf6DA8ajIfuHKc1ug+eevch+scOlSxeZjDMe3nnIxu19rn3s54jry1y9to50Ie+8+xZJCrmGU6dPHv1cvAJVdzR8CfGIhVMeza4irFnOPbdSyY0OM8gKssxQlAUqUgjPR/kRUio8BaXICEv9mOSwfGKRF155iT+8dbeCIx4DrDfaYJ1Gaw+lHFk2Jctm+L4E4Zgl40qgzFg8pfAQ+HGMkILSWurvM/IwxuD5HsYa0iRDSp8wjGi2mkh59Hdj0Y9oGEnszQ2UtSPXEq1q7N/ZprQaOzUMh0Pu3r1Lo9HgY9eeJ/Q9Ym1JipJOPeb80gJZ5qiXEpHmiLxA5ppWu0FZlgTH4Oor1yaBHwRoU4BUGG2RqkLq1yOJdYZJ6hhPDc24oCw0g0lOFNUxec5Kt86VS2e5deMdzq/V8byIUhicyGgvtJkUfVZWVo7MY/mTlzE7PaYHY2TURAUBEkUNhzEFgV/5z7abEYGwFJnH7sGUVmeVswSs+rDcivHrEefPnWR/d5fW0gpKFrQWlnj3zXeJ9ISlpYUj8/j489c4ubaMIaHQI55++iwn11cJ44BAWYx2TIzl1IVznNve5sXnn0MnY/x6B+P5XLp0jsMHD3jx8tPUaxIo6GcerpS8/vr3efHqcywtLjMc/vjIPI5lBr/3ACsG8ByS+kgkWgAGh6Akm/+XSttevE92DeTPqvlyWWoslfdiXurHzMnK1qsqws7xmE0phPgpduWjnywr5rRiOSfmgDEOa92HwivHvsQ6UMJRDyRYgyxKIuWRm5z+xk0ON+8SCEMsHYP9HZZ8welz5/El5O0KQ66cIfRDnBMUeUGRl9TCGmEQUpAfa566vT8hfbDF+Qun6KyFyGlAvbnMydNPMRkr/tHf/32KMThj2dm9RxD7xGqZ3a09ljtNWs2Y2WhCWGvQbDfY690nCBp0Ti2zWqwztQfsbh8y2Lt9ZB6isCw0GkQehIGmfbrFQr2OyQqS2T5haOguhkTE9PqWyd4MO7+9GKEqnLi1lPo9rRFwZDrFBRpH+Z6o1hGhjUY4TZKkzCYjyjyj02rgnI9xljCqBMz8wMOWhloYkuUVwcMLA1AK60pqtbgqlIFPaTRKCTwVUGqL7wfHytjGfkCnUUcKQ6wUzlhmk4y7tx/yne9+j8FkxNLiIkVZsL9/gKcU2w93uHbtKoEnMUVOvVMj9AQEPoudLjVfkUw15CWx9GnWQ8azow/6NE1RQYzv++i0QPo+Uc2bk640ns3Jc0NWGpQfE8c+h70+Bg8hIA4lp9ZaCD2lUY+ZJZqd7T1SKxmMDK3FGpI+3jEHyma/xMo6KyfbRHWfJDEsdn3iVmWa0WlX+OnZLGHQn+JJQaulWD3RotlWKKHZHfexMx+vUaJlicWnvdgijyxGlgSxYuaO1nxxfskg2edgssHDewesd9aY6hMc7FtqXki97tgeHXLnYAevGzAqZyx1F1kuIJ0NWI4dbrGNX/NI8gQPTeB51Johzzx7huXuAivLy1y6ePHIPIDHPIV/bZ4OwGFdJVNhRUUGEo7KnNsBVORCi8JQaSY9oqFAVfz/TebeH63VW15SWkGhDWlZ/BQN3jlHnudVV436KZ2QR4y8Rz/GuMo0wc27ODvv3OcUe3ecwp14T+DJikrwqKYsUegorea7b/45s8NdWlHlfGOyjLpqIEpHkqYk5VyrW1iCoLrSGqNIM1NJ6FoojOMYyRfychdUwebWhOWlEwgUmw8ecv/2PndvDvBkAx3mFEVGvdbGUwGvvvoOL37seeorbW7e+SGqFmFswXe++cdMsoSlpTUOD0ZYW/Lpv/E3eebap6nXVo/MY+s7twk9QaMGZ04pombM57/w24jC8ZUb/wfJ8JDT621W17u8tVFye2eG7yq9i7IsMa66iaj3WbIBzIZTxr0eggIpHR+GqFav1wk8xe7GPrVGTD1oYvGZZRm61HQ67eqQV5ZpmmO1xg8CpJQEUUiWW6x1lMVcCVIpfK86bIUw5Hl6rFhVTknQjMiSMSjB7qDHH3zlq3z9m99h73CMKfVjOQZnK3XB67cestD6KhefOsGzT1/gQn6KpBhjnaIQKZeunmHy+tuM9qa0kxrdTp1G/WiKfqfToTQOrUtKbREOpF85yLdDwUKtxTTNGWZjAjVDIRkkAu2HyFJzcr3BckdSJkNmmc/mYUKj2abTkAQLjpu3Nzi1soYrjn4w5042EcIghcITlX5Mmk/Y2emjS83mZs79e9vEtepm6vQMT1iEkggPRtOMh5sbOFsS1hXJdMrBVo/eZkBRluSzMUoK3vnuD+FLv/aBeUzyIbN0RBRATQgGwy3uPGwQh6c4LMbc3L7DYaoYzAQboxavfSOh7GlEmRG7Fhf2FK044IJsc2Z9nVmi2djusaw83r65xeVzJ2h22+wcHK/S+f73/K+8P8Iyy3NG0zFZUWCwYEsKLLkxCOdYbnVZbywRS/BtgXMWozzsX1E8/HDd/0crmjV3QdfGPe7EH3XZj5TNnLNY914n/oid93i8MndWr+RCHdZqrHXoudluJapz9C8fUWKdxegSi6R0Ep3OsIWgKFOSyZTJaIIsNO04RJeGxJX4KJwEJwzWlWSlRRvwW3W6C0vMth9SOkMc1ZGJT5ofreuhlEer3iaKahS5pdlYJMtLBr2cPBvgrEMpQVyrIZSPRZKVhm/86bd46aVneer0WW69c52Fdpvt7W3iRo08KfF9j0k6Y2H9JFlRZ5QfzRDrNCSRr6jHhhBD4FnAUqvXObm6wvhAs7zSJm6E5GbGnDyKsBary2oe5SRVl/HIKk6QT3P6O4eAqSj2x7wfaZIhraXwJI1mC3DMZilZkZLpEqks1knyoqTIU5zWdOIQzwNrZcUydRIMeMoH51AopmlKWWh8v8rLO4Zxe7i7x+DgkMATLLY71MMYZxxFWuK0IJQheZ4ThpV4lLaWWlDDacnW/V3GBz2mgx7m6kVKUxDWF3jxpedZO32Wr/7hn9Db32OajTl5Yv3IPIy1mLlVn3MOjEQ7jfIsAYZibrytlKIRR+QYSlUJ3QRC0IhK0rygP3QcDiWx57HarCEigfYsp9eXaEUOfYxUwHAwZTg6RCkP5WVobSjzkjxNCX2fdDbBlw5hM6wuEK6kHoRUDneG2JeEsaQ3dchxTsMJ/PVFVJnhOg3MWhccRNHRInMnV05xZu08RbmESe+SlwlXL77I0uppimKI/onjrNchmYxYvbfJ8kKfm4Mp43LG8kLMWqvLbOZ4eG+Tq595BhkWzDa32Hxni86yJGoGpOUE6X9Ib9d55/2ojX40GDkYDbm1cY/BaMgsSymNxtMlMzSpLTnY26chA37+2Ws8fXYdU8zAlcioS6O7ThBE1ffoQ0guP4qPtJBrU1FbS10V3p8em1SF3FqH470F5/vp2FXBB2sFWtt5IX80O7eUpcHNLbaOimLzXYoixWKwUUjsR1DmDNIpDzbuMxmOqTW6CGGptzo0V9f44Zs/YqHTIa7XWap1UZ5ilmve+MF19veGdNtDJibhxOXTPHPuMns7PZr1o0Wier1DxqOURr2LtY69vT5CSnThU+SVabDyBMoPkTLEOUmWpeSl5rXXr9NqvMSnfu4XiQPFjbvvEHYkqp5y7sIimw8tk0HGM1cvkWXHUJ8xGKvRFrLMokdThntbyOYynvS49OwZfJEigpDhMEFgEXOjZikVej4IdEphnayGKEJRZprd+5vMnyiN+OgO1JSG1OSoUNFu1XBOopOE0lqsqEZzQWkwWY4vMoLIUvcFmAKdZuTTkDiMUZ7GlJUmTyWdYJBSg86RQiD00V+Qza1djPXptLoIBNMsYXNnl0mSEAVzXZICgtDHDz3SLK/eJeeRFo6i1Lx9Y4u0gJXVNgtLPlFRcmZtiS/88iv8o//9XzCdlhTF0Z1fnheV45JSFEbjrCUvEqIAhOeYTXJwkigMCT2FcYZuI2Q0TvB8ySzJKUs46OVErUVWF1o0Gx6plYz2x3z+cy9w4803uHn38Mg89vZuMJ4lZJnGtwYpHWHkI51lmjjAI65HeGhEMkGlJdPRHRLr4YygWY+pRYp2p8Ns0KMRWFS9hhxMyURBsXaaKIgoj9EB39raRaCwNmBz65DRZMjHrlmStDKdWWl1WVg5zfaDu/RsxrPrLTo6pRSLDIZ9FmsFZXrA5sYu1l0k9mrU/Drv7uxjTc6djRusrq5hj/GWdfZ9chGikmqWAFYjHOxs7qJz0IWg35viRyE6z3GmZDwZ0dvYJzGCVw96BJ+4RCANs2zCeJJy4tQFXnj5V/BUvSrkH3Ic/9EW8rIq2NbYOTqlEqWqVNlAlxY7L8KPnM8fde6PirW1YIycF3KDtcVcJezRl1Mdq6Ux3blBLQ7R1jIdCnIBT125jHeiQ+9gk0++/AnW1tYp0im+cKyvLpEKy9LiAsNhD+lKoqhG09Z5Q+1QupRC1JmVGhV2SZKEWj0krB1duC5ceoHRIK1uGIxIkhFaCyYjDW5uEIFE4CFlWB1yTuD7MUk65vrbb/PCS1dZX12mvbpMLgpm+YSuqfPpn/ssp9avMOkf0Bs9AD5YQtaTEqUsKIG2Epc6ehsPiE8IFjptvE6N6XBAHHdx7h5SOqxwGCEqw2HAKQWiEnx8JEgknGO0t4+l+l0uXThz5OfhC4u0Fk8KsrwEJMZarNFIIbHkCOtYqhV02pIokJjSo3SQFBaLxVcOTIkUBWVRSesKLJHnaPkCYS2FOrqQ7+4PGCcF3dKw0K5z2B+ws7ePNpZ6I2A6myE9iXGWbreDnE0pyhzheZSFJtea/ijB3t+mNx7wlHGsrSwi+gecWl8kjiOK3JClR7+nnqcQorppKOlRFAWNOKTulXiuoB4HkJYIU+K0QZHjaUGsDEJ6GBeSlT4zrVDaUeqS3EoORjkP7h9y8dwhGxsTsvzoxufBvS2kEjQaiigQ5FlOPvVQIiBQ4DcUYSgo9sbsvXUfnSb0+3sMpiMmRYH0Q5QpOLu2RisOCTzFLE/o7e3QWYi59uwzGGNIjzG42N7cYzrJMNYRRjHT7R0GvT69fkYYxCwunaYsfGaTlNE4o9Zco9ExdJZWuPG1rxHWYpKsz+7eJsNpQjtuc3rlGUZnJvzFn3+bLHEoF4M+enfhZHVTYj4VwFWerJXhtkVYePnqNd65eYvBbp9O2KRZa6N1SpLO8KKIVtxhlo146/4DsmSI1gXj3UNuvH2H809/im4nrhqln8XRipl35Fq7v7LE1OV74xYn3nNZeX8hr36YF3NTFXJXYqwGHLoUOOsfu+zEL3BC0Gp0ePPGQ1qdNht7PS5euczLr3wGnWQkyQzpe2R5zk/evUmj2SXXFqFCmu1FrIx47cfbLD73aRKdEzcCTs0SxmPJN3/yZ1y7dhm/fvTHG8TnOd2pMRr0uH79jymKKbHfJJmlOGtQQgESZ1QlDcuj2ZxC4LOxccjrP7nB7OmCL/+tf5/f/d1/jHQeD/MJJxYN58+fYnOngjodFUqBVCAlICXWaQ52d2jXGghlqTcaeCogCFvUu03KjQOE9PGFQ2AJfB/joDQlCovBVRt355CmJJRQWkezdbSZbLvmgytRISRJgZQKISsBLukMcV3gMeHkcozvaXSZMUky8GPq9TpKS5QzOGOIAkdauPlM0iJ1gVQaU2RIcfRzuXPvIW+89Q7nz3yOwmj2dvfJ8gKlKlhsZTwgSNMEXWoCP0DrEiEknu+Rm5IsL5hMBcNxnywrMaWmVo9ozFJOnlhhMrpDGMTHPBdFnhfkeUEU1rHOEAeCdiBoKI+FThtnhwjhEQlHHEbsHo5ZbHfIjGU0zvFDn8L5JEnBUEkSC4ejgiAO+ZNvvo4fLSC8o78w9dgRRQopHUYbYuER5B6t+gKLrToTDhnv7XH4zh2yaUqRzrC2pO5JpIEkmzBJZrw5GlAPfKTTWKfJjUWbJloIklKTZkfrs1+6cIFmvYHyPBa6i5jTlna9wdLqSSyaaZFx//4WveGU3Pr0xoYrL3ya1fV17m0dcvHCOa6/Zultb6J1ifJyPD9nOj1AiYJnLj8PVuKJowv5g+37rCyvsLe/x2JjgXajCwIKLJkt2Z8NkLsbDKY9pNIIV/LSlWvsjHb5wcabDJMBsfQ57E+4Mx0QeRBpSbqbESrH9nBEZ+EEWtgPXaA/0kI+S1KMTXGiRM1KjPFxRGjjKE0COgVXIKzBl4KyzAiUxzSZgSkqtIkRZGWIMJrAV8RugrYlh/0pozQmbCwci0oYjmGo+4RBH2NnPHjY480b1/m/v/Iv2H64ycFhn7UTJwijiDwvsThCaxBYCmvx6h2e/tjHWXj6NJNRwtlWh1/8+DVOrJ9k2O/xza8USOXz4vNXjsyj7m3ycOMBfqj4wm98nDs3Nzh/6uN8589eZ2f7LdrtZTzfq0ZJpSXLUtLJkPF4SFHkZOmUG2+8TZnt8Z3xLmdPnkT5PmFD8vDgBv/b7/03XHv2kzx15tmj82gE+IHE9yVxGOC0Jsk0fuixdOoU9eYirdYCN2/e5ZlTq2y8dR+Fw8+nBDpBSoXRlXflbjGbY2EdvoBYCeq+winHrXeuH5mHJwsWlyKMlLxxe4DnCaIopNmuEUtFsxNhTMj93pTBIAUVokSB70EQORrNkNIl+F5IbzSk3lwk8KIKploKbh5OWVo4gRccvTMo0in/5J/+Y06eXGB5scuPX3+bPDN4ykNKQa1WQ0pJWRYMR0OklLSaDayrmhDneaR5Tl48wp9vsLM94MSJJfzQo12v88yVS8hjrLy0dggUAkmaZyy2YrqNiCsn2tRUxt37fSRQZik2CJklmqXlDtY5RC6YlYLBdMqsgCCsMdaKvbtDsnzCcjfmd/6jf5dXv/NdvPxo0+NGp7L+85RE3Ooz3d3lYDJmXKuzLSxZnmPKkrzQTLICay3pbEBe5HgqoFurs9TqIIUgTSeUxuALKqXLIOZgb4/MwnFzhO2DLX7vn/4+e/s9Xvjkz7O916PQBUbmbGwfcubs87zwscukWcorn/NIJxO+98M3eOrcIeurS+T5gE+95Hju8gm6rTalTtje2aLdWUSpOm+9fZ2N+zskx3iV/2f/xX9HkiTEtZjcGrIi5+zCIn/3y1+m7WDhYJPixm0WtWXFgRgl3J0O2XZjDvbvkPT3OdjdJZGGHyf3uHzuInEqyfwcP4Svf/2b3F/ZwCs0v/63f+PoZObxES87LRUgR5PMehginIgquUZT4MoEKQy6LNg92GdlZQGE5d6tdwmU5dK5s0S+jxcYXG4IQg8z1Qz6Q+7d2+b0xY9T/mtcWv5y1BqLFKkE3xG1I3KRY/wAp0La0xyLz/LyGtZoPFHNl5NBDyks2kLpGyZpSa2ccv7CSW6//iP+ePcOX/7yl1hbX+WF56+wv9dDmKOvzoGe8dTaAoaSjVs32N08ZKVzmqIc4FyC71XQwyIvSJJs7lBeyZNaXaIEDA9nlOtnGB9asskmzitpLNRY6Hb5xEsvkhf7fOf7b/LSS7/+wZ9HXEMoUEpQJBopFfVmG79ep95ZYufufaLzjsDl5OMRZ1Y6NDt1yqKsbLqEx7A/wuGIPED5BFFEEAkiBarIMcIig6OXjLkumSYlUbM1P1gE0jMgS6xyZKUkimIabShtgJURfuCRZxUKaqW1QC32MNphZW2OXnI0OosoK4jbAmxRSc8eEV/6t36Z2XTCnZs3GS+vsL/fwznwfJ/A9ymKAs/z8L3Kt9MagxJiPst3SKUqwpuxaFMR1PqDBKmGdDoNPM8hhEeaJkfmYXTFqVBSkWY5zSDGF5Iwsiy1fW7fBaMFgR9SaIOnfJzVRKFCaIvxPQoUodFMyxI8HyEs62tLxFLy59/9Pv2dAU9fvHBkHl5UQTeF5zMZjNnc32LY73Pp7AUkjixN505OBiXnSB5rKMsCO2+AlCeQKsALQoyxlSa3FERxjB4nUG8cOxL1ggazZEZ/mOJFIVG9zixPuXHnBg92Jpw68zKBCggihQo9zp9c5g/+2R9QzPbp9Ta5+twVzl14mllacHd7i3Y94ns/+AESS2kcflTDCm/eiHxw/PD7PyIIAmq1GuMyRUuNGA6ZPNxiLW4SvLWBzTOKoqTMc6wxDKTmYSdn3+0zqjmo+5i8xOyPmR320KU/JwdZ0r1Nkpkm7w/hZ7GQa2PwA48g8sniPZLZEOskQob4zsMPDIEnGBWa0eEez185z+tvvEkcKBZabYQzWFPixBRQjEcj9rd22dntUW8uUppqTnpcvPzJF5FY8kLz+rsPUK2SZb/STX7qckqZTPAVpLMJG3dvc7C3Qy0KMWWGthbhB4wGY7LZhP2Hu4g85c7dW+zt7fI7v/1bXHzqFKdPLOCpoz/e3b0tWo0uaT7h/t0N7t495HDHkcw0nVaHc6dWmAxH7PcSisAhcoc2BY9M1Bw+s2lBI2owHY1ZOblAs9Oi2YrxnT8B5wAAIABJREFUhOTGjTdZW+9y4szR8MMoqlV2cVLQ292n1mxy/tIVzl6+TBDVeHD9Or3NWwwPR+wfHhJEcO7SMkL5DAZ9dG4JaoI81fSTDOlXll9xw6cVK4Iyrlx05NFfkFQDqcX6lhMnV4hihecrdG7o7fWJ6l1q9TpPnT/N/fsHpLlj9cQq00nB1tYu08TghKh0nlUEGKRfFVYvCmkFbWxZUJqjW64rl8+xvbXNqN8jjhvkWYE1Dk95+L7PeDJFeV6FexeCIAgqLXZnqq6caldQlIbqLBcUJYxGKVIqosAhlCIvj0Y1pWlKEAQozydPJ0Sehy8hCD2QKZJq1NFqxWArlyJrPZJZidEWYwVSCAJl0HgkmcZoTRx6HO4N2Rns8Py1F7i3c3BkHkVhyDKDqgmGox47u5sEojo0srLEmBIpJVIJPCew2j1+nyokWUFZKqwQFKYk1xbnebSjGCkVfiYxkSMKj74pnT3TYjyyaNfGiinLJ2rMsgO2Dgf4jVPUG3VQElyA70s8P+Dqs5doNQMQCYVxFJwjagg2Nt+hHiuatSm1MKYRt+ku1LlwaRXE0UYbRT7B8+qUJYQ4Qu1Qs5Ttd28Q+CHJ6z9ClCWlLhlPJyAFAo/+WUWyWDAWlkxPSE0JqYVcoGWIkBE+ggWV0y42EeHPqEOQ1pqizLHOY2llgfFgQDG3VXNExIFG5yknV5ZY7r5MLVIc7mxy5uxTrC4tcLC3Q6sdofWEdKrYP5zwcKtPd3GN7uI62qmq6z+mI3/lY8+CEzihePPNO5RFTqgUflQjai/Sbreo+xJFweblc0wGfWqBwuqS0jiCxiIqalCUBV49oF6L2H14iYOtHZTwKzytJ46H28mUyeEIozOmmcNGJ4nPfIZ2rUuWl5TFhF5vyv5ojNGVf6ieU9IdDqTksN/j4dYm0qscWGZpilQSY0rONU9zuGfgmJtBGMdzZyNYWluh3lrh8tWXCdoxzXaXFz7xKTbvv03UbHDywikO93vUF5tIL2Dp1AqzyYzRYMrG/T1WVi2TJOew30dMBdG5ZdqtEOMM9pjnkuHjiwDltUA54pqPVAFlVuCsj9ESKSMazRWsGRF4HpfPrpFljiwpsMLDWIkTCuVHc6KSRzqTpAhmsUC4GOGOXkIf9A/J8wTP99jY2SfXGk95CAyNRo3JJKlQV86iBDSbTXzhoXWJnJNApKxcmkwF6UEqj9I4ZkmBLiy1ekzzGJPfR65V1lQmBYGyBL5A+h6eJ2nUQ4oypdkIcNYwHBRkKVirqdXr6Lxyugl9hxCKcVKiJEhhSEtNZ2EBp0LevvPmkXkoJwiFIERQpFNMPqHWXUdKibUG5u6SYeAjpK32Bc6hPJ8gCJFSUhiLSWfEzQbkJZMkxVOKAEE+GCAaIfaDKO/zOHEqYHl1ibMX1rBejhdItDehvnSKW/cLvvL//AlOWjzf49rVZxhEPkmScv/BPQbDA66GDR5s90gnY9otME7z9KVTKKfQrjLdjmqisos86vOwOZNpRqFbLLabrK6ts+qHJNmUsdGkoUckwZUZMktwsSSeQpjHSK0pbYa1itxJgkJRI0LELXIt6AjBemSIZYHyPnx5/oiXnQZjLf3+CF0vabe7aM9jOBhj0JhsCrakHnuYMmH74TZxKJkMeiy2auzv7hJG65SqZHdnwM7umEZ7ne7ySQo9p7Z+CNf4ZhTgXPXlanspZX+XbGhxjRZes01zvcuJ5WWarYDnr12iaX1ErbIZs1pT5CXGWJI8Y3t3B6M1tlHHNGts3LvLuz9+jecvnefsMdT4B/dHhEIwngwZiYs88+kvEC5fJLU+0jre+Ma/ZH97RG/ngHwywDmHUhWkzjqHtpayKNna3aHe9vCjkGa3xmR8CJQ4kSBEwGg4ODIP4Uk8z8PzfGrdkGeeeYVzVz+FqCmiWq2yCmufpNff50z+NqXVtLrrmFJTjwNqtQ67h3e48aDPL7xygdkwo3hnm0meoYKQ+nIdZxxWH91hRFEdqRTGVGiV2TjHUZDOSuJ6jNaC/f0RCwsLNJodrJV0Wh1kt86bNw9wXgzEOKNQXognPOxcm8QaS6ZTFPLYAzYvSprNJuP9Xd58+JAszREW4iBkaaHNsD+hcCUeCqdLAiUJAp8kr1AG7rG3K5UPowMxdz1KsxIROhqiRrN59PK3KpQWbSqi2ULbx0jJZJqzFPtEASwuxjSbEUVqUJ7GGYFSPkZAVIsQpcXzFenYoNOE06td6rFPo12n1V7iR9dvUeZHH7CR71E6jRAa63JCXxKHPsaZilSHBVctzR0KpWTlGITE90JmZUpmSppLi1z8uVewruDeG++yd/sBLa2Z9bboLDVJxdGmx4YI6Qf4fogfaJwwmKDN/e2AP/7W6ywGt0mSCVLk/Cf/8X/AxbMn+e53XmM8HnL7zg0uPHWVs2cv8aPvb9JuNtEWjFMooSisxuqqcZDy6I5c4arnTsj68hJf/u0vUR72ibf7zEYzMj/g6q9+Fn33PsOv/gkys+w0fLyFJkGzRCQapy2RcGSpZXoword7SNBo0JKCtfVzjNMRpD+jOPKimDOYtGR/z2Nvc4QvNGurJ6g1A/q7dylyzWh4yO/+3u9z5colPBzXX/8Rr7/+Gu3OAmGrw0FvBmqNy5c+RmoNuQbtHKbUiPfZf31Q/Ks/+n/Z2t7hoNdHCUOj5dP1fYQ1ZJNNXvsf/5Drt/ZYW1xh/fOfofbZl3FBgwsXztJsxGzcu8ePfvg9Nu9vkGVpZRCcZgRKsHfvNvc2dtl5uMnJ1VWufOIzH5jH/k6fhYWIZz9+jesHl/nq1/4I3zRYX1lj5+7bPHjtG9SUpbSWzII3P6HLUuOwSB9QcOvBQy4/c4Lb9+9z5coFnnnuJRY7Lcok59qLn6TZPXq0Ul/sAoI4jokDj5XVGL/u01y5AvjMshz8fbygSaPVotluUoubiKhkOp3w1jsbvPrnbzEaZ/z4rW1MrllZa/DyxYv80q/8Mm++9ROmkwnKHQ0vW149OzdRljTCBjqfcfvWWwyGe+zu7+BMQFkavv+DLgvdLmvrJxmPJ2gbkdoY40uQfaQq5jU0QLj3UfJtgLMe3jHwwwcbO+w8vM9iI2R/tySbafI0Jw49Ou06Z06skOkC7TRrS0ucXF/jBz++jvQ8jAbjDLnOKoiaVORZjsEShQGmKJECrFJMs6PxylLNGctAq13HuJL1xQY37+9TTGsU2oL1GRz0WVuN6dSbzLTHQX/CeFZZI0qpiesd5OGI5U6bdDohFwWFB2/dvMfq8hJPP718ZB42L1BGEgU1WqtrpIMdmnXJcLRPqTVap0ghiYJFrAgrcEDcAmFJTcFTn/oE608/hadiNrd2WFho8fypz3J5lrF38z4ymeBkgV87+qD/5Cf/NkVp6fVmNOsLdLtt9kufhfWcT3/855nuPSQtHIPxhFarxcrqKuOkhxEGFUouPX2J555/gW6rQVmOyIuSyTAhimpY31SHsDPHMsMzWxAhkSbjlS++zMqVFbINx+Gt25QHI8x4yLSY0kuG9Noeo2srxL/96yhp6LzxNgvXb2J1ShmOuf9wwuhgg9qa4uxSE2885Q29TdKwxI0PCSLno152zoWtjHYY46ONT2EdNzf26bYjLp67QJnPGA16nDx1kuWV/4+9N42xLD3v+37v+5717rf26qqu3qa7Z3o2Dme4iApNiCKhxZRgS7YVQHCAAE6QIB8SGMiH+GP0KQgQ5IMAAwYCWYgsxXEcC5YlkTYXkZJomkMOOeRsPb1Md1fXvt31bO+WD+d2cwbIVNFAMJGAfoDururqrn76nnOe+7zP818WePuduwgV8sLzz5FXhtvv7TCewtPPvkChax2DR6MGPxNrOasj/863/5KsKJBhhDGePe9J45hGFCEJ0YMpKzLEH4/4zjf+jGZsuPbcKzy4L/jLP/8Gt959m4311XpmX5U1fLKq0NaQ5xXNNCWJ4jPd6y9fuczCUpsLl27wp98ZI+kRxSk7Dx6wefsm4CisxQuJVBKl1OwH1C2QIwwiwqAJpsUzV55hod/icP+Yh/cf0m93WD064ng45cWnP/+hecggREhFlNb+h3l2wtH+Ju2lp5FKsrf3gOl0l0AqmmmDufl5ppMJjUaI84LNhweMxjkIxb0H+8RByIWLcziXM5mOuHbjOd584w1cebpbe+UiIAClMFXAdGTY2huQTUZMpiVgsMax+bBgMBrhUHTmziOTFr2kgxUeb/sEMsLYAryrfWL9IyLaCG9AnrFH2d07ZJoX9FsxFoeb4eanRc7e4QFRFONUQKQCev0OcRpT6goVKEprcNSeqSoM0JWbdeMebSwCj3W1+a8MT39Q60WhoCwK2qliPJrw1FqfKxdWuH/3HrG0eO1oN0Pa7TbZcExVlOAdUQDdfg8vHVpEdNoZVgRoFCpqcLh9TJw0uH51jdXO6XmEcYwSHoslMxWFkYyyek+Ar5FcIbU5ttaGsiooqgolHEtPXWLpqYs4EVBUBXNL7XrEZh1ChSxdu8pwe4tpq406gxB0fHRCpQ3b2/v86Zd/h4XFRS5evcrlK1dZW5pn9erPIqOQ0WRKVRTIUNGb6zEajai0pqgqglAwvzjHyYlje/M2+1uHNJptSC1al/XuyXn43M99eCLGUEiBFQU2LZkwYW6lw109oeVLgsmEH/zu/8W4HzH+2AL+k5f47NWrfO/11zl48z7v/fAmsh+xerFRkw7TiH6rQZoZmoVDRQEEBmF+erWVj9582c+YmdZihaRC4nzA5v6YpJFwfm2J1WaLz3/hC4xGJ9x78OfEcZtCO969+4Cs8Fy99jKldzgp6yO7r9EwzlmEO7uQ/81f+DmOTwZ4qXjr3fcoKpiMxgzGOdJL1j/7M6g8R0QBi40IHUj2th/wZ//uj/nqv/syo9GAj3/sBa5d2mA6GuK9QyAIopiimrK0sEIcKDjDXDeMJ+Sm5PaDd9nfbTAeDjke3cZlQ1wxrjnASiGlQsl6/BGG9fyu7jJr6rY1FeNBzsP3jmglPXq9NQZ2n0k55lt/8TVGwxG/+ov/4EPzSNIGQRASRzEgiAJBMT3BVBPipsTaKXFcYVxMp9PhpNVk5+Eh7Usb7O4es71ziKvVN7FIKlvP79vtNsbCx55/iVanx49f+w+n3x+qliIQYYwIIgJdS6RqJ0FElOUUKSRaK6pKs7O7x3OfnEM7iTca6wpsrZFMpSc4Z/CPuAnO4aRBeoVwp3d+a+sbmIUeocso7BAnwQrPKMsRB0ecX16FUBCECq8cRydHTKcFKoxmowaDmxG6PB4hZI3VsjWaIwkDjLUz4P4pr8eMySy8p50khEJTTE9Ik4TFbgreURQl1lYcHozJJiVWSubmuiRxUF+HIGFnWNLtRuwfF6hAUpUVJ4dTLl7scWFjibn0dNx0aTRVnuOdZnP/CF8axr5EhJK5OMUKgbaWSVmitWWYTyirgv5Cj+7VC0RxgpQSGwd4F2CsRwaSwEGlHdHyKkEIoTh9Nv3e3XfJywyH4Pad+7z2+luor36FIJB0Ogt88Yu/yjPPPcXifJd2u4P1Df7Or/89bt++zfzcMvPzi1S6IE1DwmiValxwsn2C05rSZoiwZiuf0X+hlMRHkhALt+6wXxq6/VWaRUlYVETOMogc0xvLDJ6apx83sNOCwb1t0qOc5aDFZj7GlikbF+eYDguOdgs6LcX6fJ9SO4w1WP1XdLRS66kw26hXVN5S4nBIpGrwzp2H7B0e8eyVFc5vXOThtqTT7ZAVljffuUmQtHjuxWdJGvNk2qCtQzlVFzxRzyUf0WdPi2sXz2EvnGM0nrC7dR/VTzgJSqwJMZUjnlvCe8OgGDI1OUE2Zmd3jx+//n3OrSzRa7UYHByRLfSp0XYhxlgm04yHW7usLZ0jCSScgY64/tRVDqYnHE6n5FmInRbo4SGUIwJXor3HeQXO4Wf67I9CSkGofA1RpCCb7vHa9+/zw9e+TbPZYfn8HJ/8G1e5e/st5BnLziSKieOkXqwJCMOYZhTg9DGIlH63x9HEA4p2q02r0+b4+Ba9bp+trSNOBtPaOd77unt1ltGoot2aJ88s3f4Cr6wuMzpjVt9odUGEeBUghSdpN2v9mCKoqfUIgjAEU0PzsqxgMNzHeYnEgzdYW5PDapXnEIhBqRpeyRwIgTzj6Ly4tITOYpTL8DzEeI92lspohuOMXmeCoZ497x16pqOMSlukr7DO4JxBqVq0VIgaW260BUX9pq8U1tfIldPCWUtV1dru3oNFEUYhgdIECuKkiZQORf0IFNqTtBVJKolDKDPPweGY97aHXFoKybUhLzWduZjPffpFdg63GY8OifTpI55pPgEsSRpwNJkQ5EPCRo/t/QFzz69zaWOVUhuiynB07yFhEDDKKxYuX2Zx7UItqjYjTeGD+vVwUGlDVpQc7gxo9WLmFk9fQgsVzRbgki/9yi9zeFJr34/HIw4Pj/nGN7/BX3z766SRJGm2mFtc5mPP3WBxaZnPzi+ijeYH3/seyk144+YOh3sH7G1tEyURIrB84Rd+DmM1P2GJ/79Hs9uidJb5zPGzOxo/OKDRyFk5LjAOXCBorS8TvvwMohPREDHvvPMWg+1d0lFB18KelCgbcfV6n5s/vsfRYUURgmq22BsOscoj3Omz+vfH/w9aKw5tLMbXW387UzMUFgQxu4dTpuNbnFtdZP38GisXrvDdV19naWWdy1efQaiEylisNTMdFllrruDqI5HnzI5cl1OM81T5lPVzK5xbX+frX/sax4f7JCJk82SAc5bCZFTKoIRk+71NrC5ZWF6l02yTT8Z1d+gdRwfH7O3vk00LwjChGQcEgnpGekrEEcyLFoOjIeXRJqK0KC+obI61U6RQ4CwqFLR7LaxzJI2IZivB+gpjNL1eh0YzJG0EJEnKdFygM8vJ6ACdX2FtZYP93Yen5tFqNCnLChl6fKjIpjnYkio/Iu4skaY9QtnE+QBnpoRhyPxcn83NHe7e2/4gk1YyGyNAHHeYTjWD4ZiFuIsKk1PzqPIM6yOEkoBGyopWO2Z6IlFS4pwkTloIY4iTDq1OF6cLlIpq0S4R1sJOqh5FCZliRfpYeA3fwAmL4vTOr91ZIFMJUSD4xCc73L79HqPRuwTK461gb/8QEUjCKGI6KciyEhGECAnOeKzWhEGMsQ6EJ4wUSZSiqxIEtLo9zp1/ipX1c6fm4bwniiKqqqLQnmkJVkR0mgEqsuSlwc5kA2xp8VLQakfEMVTGUrqAhwcZO/sFz6ylTHTJw/2Sj68mfPGz13iwt0BVjNndHp2aRxAHhFFMI00QSlAJz8Xr13DhfdK5ea68/EmcEGRbWxzdu0fSaRIUE4JWDycjnNE47SmLApU0qcr63jauZt622j2EryjK099Qmq2aUeu9Z27eUPmQoNFmJVBcdZYyG6HLkjLPGAxHHJ2M+JOvfRPhFVpXdDopS90Wy/2IcdUmTBLOP32NxYUezhrybIrWBd6fcVJSBmtKXKlJBpYVA3I8IppWTI3DBQ3aq5cpqjbHJ54sENzNjzEnE9bDLiIxbNmCyCXcv/+ALJ/inGKkSwbKYAdDZKCQ6q+oHnlZlWjLbFtcHzVxroZseV87fMiUk9wxuDvg4XGOjfucv/YiqyvreKEwRmAteAvSi/qNYEbff1TEz+rIhRA1+iOI2N0/oT23SqkFt969zVLa4PjwmN7CHDKqt/WlgN3tLdr9ReJGE28spio5Hgy4efMd8jxncWmRjz9/g4V+n4VOE2fOhttdufASYRBjJ7fw02+iRAvjAjwC6y1KwNJim2s3LpDrnJ2dfeJmDKpe6JqJobA568uLXLy0xuBkQBhJiiQjTFoUk4xmKyY4o4B672k2mlSlptVusrbcocqGVNOMqtQY1UJGfYQ8Qvox5fSEZprwxpvvsbt7NOtfau23R1K1SatF3GiSJAk3f/QjbgWCo8PjU/PQ+RTjqrqQCwNUzHfb2IUFwriNFwcEKiKIAq5ef54oadBoraNUgpIBUoa4mcuKlBKEwrj6DV5IUaNhhCUQp3d+mWtD2sYFAddvXCFK5skyg9ElYSDZ2r4LxqF1RSlnyp1egBMIFEpGWFdLKkshiOKQRtykyATGV/R6c3S6y3S6pxfyNI4RIiAKBNMsJwsUg0lFIxU4FIPRkI2VLlGgGeUlKgiRUUDaajA+HHNwkpObBiIQaAOTvGYcx97iyxPWVzucHEk2H54umoWxiCDAGOj2+gRxSHduheFkjBOC4XiMCGO090wmA4LVZn26UwIbOIqsxJcVQoHWjmJaEgQC7yAyjmQuoao04oxleFVmaKMx2tQ4fWtxTuC9RClJo7dYH84RLHsPzpDpClsZpqNjVpfnuXZ5nflOiAzXkElAsxGTRIqD3R3uvPs9Kl1iz5jVy7AiVBatPH/08F2ebS3w/Ll1Gu2U2EAzbbEYdPF7jkJAqTyv7d1HHWUsuyajMCElYnI0IssqwjShpSQ+1oxkRqME6RLK+KdTYYSPuJDnRcE0r9AOglB8QKL2kcKhc64WO8JSHA4IogYrq7USnrU8Ft2q8bk1ycjan6gk/jSF/F/+8Te4/2CT4+Mhm5vbjMb/FO8t7WaCthaKgnknKCcFera8urO1wy+88AoXLlzizrs3eef2u3Rjxa/94i/Ra7WRzmO8xYp6Xm5M7Tx0Wnz7668RRwnfe+0NtL4PqoXxGd5XKBWy0Ovy+S+8Qrsr2Nvfo5UGhHGP/nyfIAiYTkqGw2MoPXffeY9AhRhtSOIGr7z8EsPxAUo51tbWT80j6J7jmede4uOf+DQ+P+JP/s//jX4R4jsVg+w2a2s9pHC8/farZKWnyOH2nT3evb1NaWtlQjfzPPHOs7wwx9zcHNpa5loJzlRo7UnT07VFvKmQooYHBipEELC4eo711XNMTY+qyLj/7htkwNqVF2g0+8gonJmRzMZQssbDa+dw1qNUgAzETHDM4IXA2NM7Lh+cr11dwgjtS85feZnFjecJpCVShs13X+f73/8e9+7dx3uLCiRhIImCGIejMCWlK0mbLXSpCUWIADYuXiQIQ9bO32B9/RmC+HT4YSRlrQMeOnLpiaOYd+4cEYgW7V7KZNSgLEpEGrE3CZHKcP/hkFvvGX58d4p2nu5cyMdvNPnenT22x/D0UsRylHP7nfusPXWOezc3qfLTDR2m2YjxFKbjggvXryPdlLaC6y++wPTBQwaDMT7McYMhVSnozK+QD0dYb5keDbGuQCUR1sDk8ASbC8K0xskH0jOtBigUxej0E+zWg5skSUJVldzbGfGjmzvsHmc0W336vXnWNy6ytLxKs9kkSSMaacpKrAmFQpcZ59eWeO7aUwhyhE2o8immKjnY2uL2G68zKnfR5uxC/vKn15BxSKwke5OSP7/zgE93L/LJT32RxYWY//Wf/i9Uf/mXuKyiQURCyM20zQVf4k1CvNAhjTsEzYpLV1eIIoVSAUncRAaOubkWgWqg5Oknx/fHR+sQVNXWbpXxpM3oMU72g4W8dvp55LBRd1chuppJ3LqfFH9gJn/7QVu4swr5a6+/hS41lTaUlWY6GXN8dESWTUDASrfLYTal1BXj6ZSyLCmLkuPDAy5fuIgtMz727DNcu3CeRClsXgACGdVuH4//+TMK+auvvkaapBweZTP5ggLvDVBrSgshOTwYMh6aWi2ysDgzpooVJVBqS1lOyfOKl1/+GGEY8f3vfx9rNMPRhL2DI9qNNu705wNdlmijGYxGKK3xqkVlQx5s7iLEAe3WUxwfH+GRdPp90kbA4rJDRXfRPkNK8VhfBQ+ddptXPv4SjbZEqrq4mlITRad3wqgQKSOCOCJJag3wIKzxyZVpEcUJ169fZfP4EO8dURxT2QohVE3AkaqW0xUAM1s0GaKkrJeLQsxEwU5PI515fQpvkSLAepAqxIsKYydsLPc4OrfMaHiCmeHUO62Y6WjMKB/XKJN+j6WVcxwejun15mm1uiyvrpCkKf25dYKogzhjuQfMWKOawjgmRYUxloMTw8pGgjM5WSHIrGdvpGkGjo3zKWmjhUgSqkKztJDQaQi+984h1zc6PLMSce7cHEfDAabSLMzPkUenz4S3NvfIJ4Zzq3N4b7BeMB4MCGJJ5UEksu6OhWPqDYtJQBxJlPAkaYuDowlKeDrNNrIfYqIQwzEOjVVBre+jBcKeAfvLRjhTkKQpgYKNc0s0myV5WVLlB9y7M2Lr4V36/XO02h3iJOXcWo92q0sYNjkpJTdv3kG5EXdu3mJvd4fdvX1u379Np9/i+Reu4XHYM2SwG02JlYYwCmh2unQzyx9+49/yR3/6TS5dWmDrZA/nVF2vbIX0gnPNOUxZcFJNyWSC8xVClDRaTZK03pVIUdJoJKRpAj54XyE5Oz7ijjynLEuMg8iqxx35B12AZvAHamF17wXa1ga0tYxtbSrxqFi/X7Mc+MDHHxbGQFUZxqMxOw8fUlY5wnlCGVLoiq2jI7aODh4XYuc9Crj91hsoXbE63+PKtWeR3lON63m7khJTeUQUEAbJzLHo9Nfj3uYuQjighRD1OEWKqBaBFTUaYTTMkEpjXIFzjl6a0Ol00FpT6iF5NkUpxXRacXiwTVloWnN9Nh/sMh5nTKVmrtM9NY/5VsLm7beZjE7otbo0W3MkzSZeKQIpyfMKK2I2rjxDZRWjYcXckiUvy9kIg8c3nQCKvEBrTb+/gnYV3vmaaXjG0lXFKUI2UFGAUDEIgfETyiKDWGJKyzQbgzeMx8d4AtrdeaIgRcoAKQK8rN1BnXMIr/HC1/Nyb0FIhPSzReQp8fi/5GqlRBHgZE13lz6nF1RcWZ+nqi4g0i7IkHx0yLH0OJvR7UZcvvgUxgW02hvML63RbM7RaDaJ04i01ceasx9U5xyV1vR7DcaFxnhJ4RXbR4YXoy6OY0bTEu0yxpMRzf48/b6kkY7pdxVHB4YihzclDsfEAAAgAElEQVTfPmR9cY7nL4RcWe8wyR3LS3NMhyNC1SCdP/3+ECJEhZKs0tgywxvHw619FuKQiYXGdESj18UEFQvLLTpzXXQYkk2GbO3cJQwC0qjPeDTGaEMchLUpdqlxvnbMKbIKnZ+OJirLKUI4Go0Y4zRhIJnvNxGigfMO7QWVMVjzgKJIyXLP4WGKkCGNZpNmErL1xqu48oTJ8AiERIYRIlKkokVZ1WOZsjr9urTbLRAOpQTSwcZiF3Mx53BnzOHxAyIVo6kVS03gMQhGpmCEYtxuMAhKglDTbsdEUY1ES+IGYRiiVK3NEwSc2Qi+Pz5yir7WGuOgLOuH6VERB97XnYt6U//Y307Wfp/W45yYSdk+Kvo8/j5nFfBHce+9e4xHQ8osx+gaWaKEJFYhURhTmoKZf1xNI44i+s2YXq/LYqvB+fk+DVE7GVWCWsfbO0qtZ3owAilrCdrTYvfwiCSOCGRAGKT1clRIwjCY/c2KqqpwGPKyrAlBwxLEEUopxtOCPLMYU3L39kPG4wlOG/IgIy8LgkChBbgzZm2ddovKOorxESaKaDRbICCJFUmasnL+Cq1GwuH2HcbjMUExpdFJabUSRpNJbZvnxcxLUjAdj9ne2uKp6+vkeU6z0cBYizNndDqtNtZGiEAAIVIaqspzdDygdBN0UTE5OUAjMYxxMqbVXkIQ1JK/AoRUaOvAR7XJii/RVUWNuZF4Z5BnaL5oZmxMIQl8vVwTQUAQRCgSIjTnluYwMsYnXYK4xevf/XMubJzjqUtLJHFAt3WOrZ0hixdfIYjbSNlCSEkQCawPECrAn7EMt9YSxyHOWmQQUBUGqyQ7Q8MP39wFNNpaep2U6+2Y5cUWK0sprjIMjyZsPxxyd9/z4GjMF17osrbYpr00T7Z9iPC19V5Zlojk9OvivKLdS/FkjCY5cSzJZMXO9j5BHBJLh3CayNaSx0qBjhtElWd8lBEkgrAR4wuJKUrSJU/kQ6IwpdKavMxqzoU8vZAbrSGOsVYznk4wJkSoCCUUgVREYUjDh0jhEdIDDk1Wn/KnQ5xLWFhsMR1XnFufp9Hq0O72QQRM85Js6mq8vD+9LLaaHSSeQHhMpXGtkvNXmnTnZb38HnmM9mhdS3Zb65jYglHYY9jpkLdGLLZSOq0GgYJAJkhSAhUiZT0StGjcGeJu74+PvJAbY9AOYhd+oBuH9xXy2fjkUY/32OrN1TPYRx25927mCOQ/UMir6nTYXzUeU4xGtRWYAOtsrZ1Nba/ViiLSKKLdbDHX7dHtdFhZ6tLpdOk0WyinqbIci0QFweOThXQW7zxVVSKVqlUdT4mo0yAOA+KgCcOydt6RtWytm+m0D0cjoiSgNAYZhmRlwWR3pzbkrXT9Bucco+GYdqeDqSR5OcViWVs9Tztt0IxPX3ZOCku70yT0FqMzWmmtRCeFQQaCuD0HXnB4cIhQFVHquPTUBp/5zCf4kz/+aq3VjQRfO6HmecHmg00m0+cI4gAHFGVJFJ0Op9K5rufcxuNkiJKa6dSiTUiV54RBSn/hEq3uPM12lyht0G71CaTCOktZFoClshYlY4QUoMVsCW4RXmKNPlM+1mmNc7U+iY9DEDNatpNY1aGIVtBeEnVLphWUlQIhUa5C2II46aKtIenOMbe0gfASISOEUFhvauEzZ7H+9MIVJwFhpHDaopxBhRJtKqzzvHVrj8sLTRbmupw736LZCmlEjsnEMBx4bt2peGez4uHYY1WKDANy2eL+XkFoI7bu3uXqlQso6ZHR6RZr1himowpHhlIpaQfmbA9Tebr9BmWhIcgpxxlexJgiR8130SZiod/DqwpnTC1eFioGRxlxEqDiCONrL1CnLaY8o5AbTVWV6CpiMhkhaBCEDlRYa/W7ChBIUS8/hYBGHBEEIUIIlhYXWHvhGlVZ4D1oY0Ao8qLCuoxiOgBh0Weg3oIgJJiZt4tYECtHL2wQdSSNSczOfo4ynqgy2MLgteMwzxnLkMMoJOyGdNqKNI5JGxFxnCJFhJQQBAIV1J4A6ixA+/tz+qn/5P8HoWa42UdylVrrD8y09ewBeoQFd47H+HBna1/GulG2jxejzv9kzg7Q7/d59tlnT83j1z7/OfKyoKhKBpMRw/GQqioJo4g0TZnvtGjEMd1Wm1gGWK3JigLvHNVkglKqxsJ6jVKKR36iwtfH79IakNQ62KfEWtql3QhQIuG9akQQxSgV4gNFUTqabcVLL15jodtgNBpwOBxzf/sAZ8BMNWEkUXja7Sbz8wGdrq1lYJMOV689zWRUsr52gW6nf2oerWZMMR6j4oi4lVJaS15ULHTa6Mry5ne+iZSS4cmIQEUkqSJOPF/4wmf50Ws/4u57D4ljxec+91kWlhc5v7HCU1cvImMw3gKKdm/+TAu+qDqgMq4eVc33EUhazYAoXCBZ7RBHTZQKCaOYaVEiZcQkGxBHdZE0zqKLgiiJEXiKMq89JEVtSVeWY4zQmOp0dESWFzVjNpQzrRZJLYGm8FIwiV7BWkPU0IhAo03JjRcShJ1iTYVzgijp0Y77qKAL1HuPGlEhHy/n3RnEJG0dJitQHpT0tDsNYh2hU4eQkrc3jwh2HSsnfbpBwmA4ZmdQMS0dZf3w0EhrU+rd3OE2d7lwvs3cYouyOsdfvLnDMDdMc/iNf3RKHtWUQuc472kEcLxboCJHsx3jvefhwwOSpAnWE3ZXKUtodZpEaYJhgNACazUqjRHCIaVlmhvUtF5ul8ZgkegzTmzGluSFJ04CWolCa4sSBu8FRmsiBN5JkAqja/lf4yrKWVOUKEsiLbryaJNgbe3d64BSC6SSxKHEFNNT8/C+wqsAKwQOSZq0SOIm3ZbDzlsW5+ucEPWoxlo7Y2HXekZC1pwIKWU9vgsUcayIohApRe09K4IzR7Pvj4+0kMsZOzGY3b/vX3SCf19H/niyAcLXrLxH3bibMTkfd+EfRKwYY850SVdYGnFAMwnptlOqpX4tfDVbskpAidqqzPt6gVcXC1ErNT6axzs3W276mXqgIAjr7byQAm9PvxL/zX/1X2OqjDd+9B7ffvvPkXGI1Xp2rFfoSmF1gxvXXmZlscv+4Ih7D+9T5TmDoyOMrQjDGClDVpYXOX/hHIOTQ0aDE9xYIwrH8HBANj5dLtVaR6PRwkqojCZNmngkxmjCUDAZHRNFEXGscNZgjCaOY7pzXZ554WkebO/Q6Xd4+dMf5/zF8/T6LTyGypSU04x2q48xhsnkdIq+LnepjEYGMaH0BCJASkmZZ3hXEseeVrONVJYsO6TMHO3ePNZmWF0zhqUIsaWspRqqEnxZL0C9AJsjpKXKTy8YQqrZnL32oxQo6k4vqD1VCZDKEYgKTw5InFtARj1iVcMgg6ABopagdf7RqbLe/wgxaz7OGAVKalKQlZIgiBiNs/rvCEEjTZlr9JhMcn586wSDIAk9zofIMEU4i/KGZiRIE8XxcMzVjQt4JG+8s82DrTG3H2Y4qZDB6WiisizQFagoYJyNCZwnUg0KLWgHgmargdMO7TRxWr9hNaIGMzl4JLUjmMksVlCbpRSW0llCFFVZUXlBeEY5qhUl6wKIMwQyJAgkQZgy0qZemjo546bUjZXVmqIs8M5jjcbbur5YBNYLrPXIQGGsQWrQHow5nahVG4zIxz8e1QMpJeBptuKaja3UTInUYowlimqAh7GmtkgU4nFDaqxGWQGox9pA/j+ikn+0zE5ErUERiA8UXzfrHh7DCB+PT5gV7ke/V0P6HiFc6oWieDxqcc5xfHzMa6+9dmoedsa4lFIinCWREuIYo81jy644jHDGUnlbz35np4lHFHltDN6CnpkMCCFwoqZyi9nFlWdQsBtJxPF4zPb2ESJIcdIipMMZh3OGIGjw9PXn+NKv/31a7RalKaj0BIzmcGeLk6Od+kaykmajTVFM2U830fNTjHX0VtZZWTuPDE4f8XihUFGCFB5tSlIhSNOEyfgE5x0LiwvoquTo6Ih2q00cRxRFzjif8Cu/9iWW1tZotVqsrC+zsDqHkJ7hcIyYSbzi69NJp3O6E00Nu6yXklLXNmpOW2KlcJVGGIGbmWY3ogpnCkJRyxdoCVXlEDLGGYlzikDUnWxZljXXQBdoVyHtGSMeY3ASnJeEyiAF9eId6vFM7bQLyhFE1N2T6CJVjZxRQiFUjEDiXFFrsVs/W9Q7zKzheLSf+bBY7rWpqorSe4rSIFWIoi5IeZ7TEgGtRosToXDSEkagM40StXtWHMQ0ogApLNPK89a93dpKbHdMaRRRo4NU4kwC3cH+iKTRIwo8UZSgvCeO6uKXTRxRAuPJlDgOZlqIBiEN2biinHETnBVYb3AyQBUSKRIwFTaoYZ5VOSUrzxiJViVJEtcjNO9Q0hOFijBOiEtTyzV7h/eGJI5ot3rsH2zX+jZWo6uCqqxwXpFXOc7UTGBpJUWZE3lPKBXenP681LVntlAX4n1AC/BekKRR7dLEIxUGQRI3CIIA7zzBbNnu8URR/HgyobWecVxqYIf4j0CtiJ92QfgknsSTeBJP4q9m/PTyWk/iSTyJJ/Ek/krGk0L+JJ7Ek3gSf83jSSF/Ek/iSTyJv+bxpJA/iSfxJJ7EX/P4SFErv/3bv+2LosAYQxRFTCYTsiz7wMYWePwrzJAls88fbYkfOeU8+vqjjx/Bebz3/NZv/daHoul/+ZOf8Y3uPPfGlmc2IiJfEAiJcB7pwQSm9l00Fm9rkk+pK4wTZEXF/QcPSVpt1i5e5pnrT5GEiqXFRRrtDt/599+mHB4Qylpx7x//i29+aB6/d/Cmj4KYYjRlfOcOJ5v3KI4PiKqSbrNFurzOoKxIe13ml+bRtsJMLWGvy87wmJtvvM3x5h4urxhMJvTjhE8+9xSryx1ub29xr7kMScQrH3+ef/jc5z40j8//z5/0a8vr7OyMWGi3OT48IiumXDi/QNRKyErP0eaA7krtBF8OHFo70naXMJI00phiHLG8KtjfyznY2idOOyyuzWPKgtFQkMYjVJrxJ//D9z80j++OSl95CKRF+fq61+YMEk/9ucJjigmlgrjZIKjgg/rRHi/8Y9WAn7hHeaSTGAnCej7Ziz80j1/9wg2fpinz8/OMvGd/b0CeWYpxjtIVv/jpZ1joNghiELYCFTKoKoajYw6Gx0y8RiURKlAYqfHCYUyJsZrSeGIl8R6McfzR/7H1oXn8+n/6mz6OE8I4IJ9mRIRkZYUMFZEUlA5UEM7gryDwpCrAWUuel2gHVgiiJCBNQkxlSKOQo1GGQbLUbeCcZ5wX/Ks/+J0PzePP/scX/f7QcOH60zz3N15Ghp7he+9yuH1Aq7vED1/9KnlmKCchwoMIHFXokDTxhSVKJqSNlPHEM8hX2Dn0qDSi0gWxr3j6yhWi1LN3sMN/+49f/dA8fum//CXfbKXEcYsyF3Q7C/ztv/UbPPvsx0iCkNiUGGpzj0AIAgT/5mtf4ff/5T9nf3uXbDym1+9hKYjMEcfjCVeuvsCNZ15k4/xVBoMCaw1rK6v857/+Kx+ax//03/8XvtvuMB6NmOv1OTw+5Hh4wvFkTGElk1xQZgWLC8t0enMMsilVKXj+hWf5u7/xd1g5t4rH0UwbCBzgkQL87D5+v/dwFH34ffr++EgL+Wg0oixLoih6DLkBZj6NP8FoPzIYttYShmEtGGXtYzifEOLx16uqIkmS2ix3NPqpRLNcmjIUMclCh1CcEEhV48YFdSFXrj6qKI/BY6QDU5OARBCTGc/T127w8s98lte//yrHh3sMTk64v7WDtZq/+4ufw5UTVHD6gWdnd4/R8YDDh9uIrYf0pGcuCfGNPmXc4MHDLbLxmPWFBdqjIakzHJSWnQeevemE8d4+yhgCqWi1O0SBoLIVYSgJJKiqYjgc8Jdf/Sr/8LnPfWgenWAep3OaDU1lMl6+9inKI8Netk+yqBmcjFhYbDOdDuj2E8pGyWgrYjLMWdzocXi8jxm1UFETpRKa8Tyj/Zw984DF9QvMdSZYP8/h4ekwNytmhgvCI2QtNuG9Z0afqAkWzrN/6z36aytImeClmrnWz/C8UuIe3frv1+CpvxtW1A73p8VgGDMYOYajKYXU7O2cMJlopPWcX+rjZYxB4PIMdEVVZQSNFusL6wQi5r2DHax2WGfJ7LSGzKLQpcB5Se4qpJRnioipQBEHAokmjSVWOyrrCFRAoCRZPkWIkmaziXOOOImxlaHblPTaCTKKGY4LhhNNUQi808SJotkMiOIEXXhUEJCcwbg11pNruPNgl+fDBOsLwmaD7vIiSTqH8YruXIM8kpjCkqYGLzKMKaikpLBt3turOB6PGA0jtg8k5y9fQKiIKKiwUUJ3aYWjM/D9pjLk2YQyn7J+7jrOOv7iW18lUIqXX3gJhARZi82JGVN78+EWJ4fH6KLEVgZTllRmQhpaMBWHB5scLS2wfm6DdqfN3EKfSxsXTs3jZ155FunBVZp2kuA4x9FkzM7xgM29Ife3BuwVBVrnNBohMmxysD/hzs2bfPmP/5SXf+ZTBEnEtStXZvDQ+t70QuBlzer2gLfuTDb0o/hIC/l0OkVKSVVVZFlGFEU8/fTTjMdjDg4Oalr47IF8/8ePCvujQi6lJI5jbty4wac+9SkWFxfx3vMHf/AHPHz48EyKfm9pFeM9zaSLxBKKIUpIpK8LRSIlobUI78gxjKSmVALrYe3KZZ759Of55//i/+bffP07SGeQ3uKdASwrS/OU2pAmzTMptt/8nd+lv7AMBAS6wrkS4Zso2yYbC6aZ5OQop5WUrCwmFM5QDUYsLS2QJA3y/RMGxQSpYogUVnhOphnexzR8xI//8A+xYYRXEv67D88j6Y5Y73+CzXfPMXlwwoNmA+synL9I+p6Aja9zLBx2GDLcr8iqnJXFkOCkwWDrBBuD0JZEhLR7MZaM1avLbB/eZX+ySXEwZPXyAlHjdG0RoQU4EO4n2FwhJBKLFLXsweHWQ3Zv/RgpK+bDgCiO2d68T6PRxHlHu79MEDd/8mY+qw1CgLcGIetrfFq8c3ePMIwIgnHtaiNmuH7hQTru7Rzx3sOSfqdBKxQ045idrSmLfcVyf5X5fo/v3PwBuXI0e00qp/EiIE276Lxk/1hT6QLvTydI5aUhCgIiFNY7xqUjaaYIJTAIev0mVjWwWvP0xiptVzItB+yPPcon5OOCUaXQSqCsRKiIcQlV6XHjik47YZQVWH86AWZvVLE/UuTHI776r/89z336M7TkCs25gvb8PEHcZTAeEqYBlVfoSuJMwsHRlGaryygP2RkUuHiZr7w+IIhSpumI/+w3/z7PPvUUv/97/wjfSDg43d+CYtikm8xzfLLLSXRMmjb43qvvsnn/Huo3/wGfeOkV3nj7Lj9+5x6fe/lZImn43g/fxouY0eSYOGgAMfn0kCgsKUvD4OSQ177/H7hza5Nf/qW/RyB6eHM6gc4VGdM8p9FIkUmLQEbMxQHNXp/5Bc3B9rdY6CWcv7xOr9+hETd4dfoGbnxCMB3ynX/7ZSa6xPz851jduMzy6joWhZQgfO1+VfNiBM3TX5LH8dF6dlqL1pogCIiiiBdffJHPf/7zVFXF5uYmX/va15hOp3jv62PtaESWZY/JNY8YlEIIvvSlL3Ht2jXCMKQoCl599VXW1ta4c+fOY1nbD4vthw8J9IhJcMz8tQsoOUYJiXCgJOy+e4eotEjjyIWDuRY6ijm3fomFtQ3+ye/+PseDUT2OoX7xhYfz5+b52PM3iKIAJwTqDEuxpKq4/+ab5JWl3+2CKdmNAhrtEZ3+EpPBAJsXZIMJ9+5uI4HG/DwTC2HcYePyNby8z9HxCWDRwLg05F4QJU3mGwnd5VXWLl45NQ91coMHr61z683buEAjDqDZbtJuNHlPKJKwz1Fxn0ZjheWNFrdujzk8ksy3QoRz2KBBJkYcHIeICIwoON4yBCJlOChZW13G2JByfLqBgbAG4QQ4h3vcOYuaZCIgsh5bGIJul4ULFyBKsHmJKAqE9Ny6dZu1y8+wcek61s4quJ/9NDMccDNdnVPD5QTSE0hHVdN6kNISSY80FcUkp6pKhIwJuw3QtS7+NMtpJtBqhzxz5Sp39jcxUAsrUaKwCGGBijhRqPD0x09XhjJ0+EBgvSNQgmw8JTeOTq9PmsRIGRCEEm8yvH3ASr+PCjRxNEWqiP1hk61RzCQ3OKOJhCSKI1xkMVLgZIwuzzAwkAGVVTjZ4PBgzD/7va+zupzw9PUl1nTMtadfYuv+LbpLbc6tP83u1gnf/s630bJFpS3nzrXoLa7xr792m8w3cHlOc3DCe/e3uLA0zwtPeVrxJvH66frs589dYmNjg5vvepypKLOKbrtDPp3yo9d/yEsvfpzRNGdaeL7+Z3+BNGN2Dgc4kfL0cy8zPD7h5Hif0oCLQ6SKicMW3kjSKOXoYI/b777FF3/+C/Dccx9+XbSn0JZbb9+kso40TQnjiKLU4BPm+x2aLmVjYw1jPJGUXL60xtG9bYb7u6S9Hpu33+H7DcHq/hH/yc/PEzXaSFtr+tRSJJYzFD4+EB9pIQ/DkDCsxbKCIODevXu89dZb3Lhxg+vXr7O4uMh0Wusc9Pt9vvWtb/GDH/yAIAhIkoRms8lkMuHZZ5/l2WefxXvP/v4+3/3udynLkv39/cdz9NPCGuiHkrwqQSYIFdQPvISq0gwOj1HTElFZMuHoxoq5c+dZO3+Bf/K//zPGkxzvajd2KcBZzdJcj2curZNIW+sLCFVbtZ0SLitRVUVTSryvIICJrQidJp+MODk+xBvDYKgIVMC5c+e49MrLWC8JZMjFRsrVTxXcvn2Tr//Rv0IqSZFGTHVJ1G7wsz/zGT7/hV/k/MblU/Mo3rjG8PA+wpwQhRHj8YTxyS5HQUilNc+nN1ha6rOr3yJ/0EBKhTcp2UQg5wSdOGJO9nGmw/Fmhm+UKGNJOoooE8x3eoxcwJ2Ht09/PayudZxxCOXqCyLqUYnxggjJ8f4J/cU1ZNSiMp7YK3RWIgNB6D37uzucv3AFby31Ra154t57vJBY76lV40+5P7zHzEyPhQTpHYGAWEmsrRjnBeApS01WOKY6pxlLEmnw1mBKwaWFFQajA3arAl/M5Jh9SVWUtBsNJlnFGdIiJHFKVlT4JGG+t8DxyRFWl3Q787QaTebbCWFYcXIy5PatV/nZjztSmZCIfUIGtIPzNBc6aOeJ0h5VVVFltXqhx3I0mhCohDg4Q0TMaEajgnBujjAJ+d437rK4vs7OWPDxKfz8J86TJBkyCHAup6gy2q0FVuY0raTkaKJ5527BrQclUxMSCEMjSdjc3uX+do+ryzlJNEYszZ+ax/7eFlcuXabVaPLiSy9x8+ZNJJLxeMrg5IijoxP2D45AxUQqRU9GGBRJu83SuQ1++W/+bb761S/zo9e/SVYVqLhFGncZDad4C88+fZ1v/dk3ONndOzWPQQG5Cbn54IB33n0T7UocCl1afCX45S9+hoX+HK7UdNrzSClZsJbe1ZTd7W32Ng8II4d1Yx7cucX+sy+ydrWF1RbpPM7Xe76zdNHfHx9pIV9dXaXdrmnHOzs7nJyc8JWvfIW9vT0+/elPMzc3x/z8Ty7myy+/jLWW+fl5NjY2aDQaHB8fc/nyZbIs46233uKtt95ifn6eixcv8qMf/ajWMjhDHvTEtlBCUWhL6TShr8c1UghcKAjWF5lvdwmdJMsK/h/m3jxW0vM68/u9377Ufqtu3bVv973dJJtNdnOTSMqSKFkiZY+jUSYaW3YMj5eJkwlgDGYCB57BZJ1BAiRAggBBEGQfO56xDI/jVZZp2RJXkU2yufXeffvue93a69uXN39Us00hUbUGCIgcoP9ooIE+9dVb5zvvc57zPEGccOKRT/N7v/8H7OweYNoOJdOggEIrPKZoKCy6DkGnw9DrMD+/TEW1aPcnO69UpmqYtoHnDVF1QaFSxS1VGHUjdjd3iYOxXECeBfhBBz9SWfxSg3CYcO3SNTJD47HPPsWDT06z+f1LbOxvkAQRuZTUZ5o8sPwgDz34EF4wGWrC36BkxMRmRhB0sVWVNEkYDUfomsbtd9f5/Befp2Q0uLT1MobjUGz4eN2YRaVBSZ2Hjo4dujj1DW52W5SmbKIko1aSvP3eFZoLU9TK9YlpZKkky3OEyNFkOoaE7g3AARVG/R6kBkoWjVETQ0fVVULPp2wViA2TnIw0v/uZ7+p95DJHYJDdHS5NirvWr+QSDAG6IrBUDdtQydKYLEuQ5Oh6iqElmCo0Sg6SHEuRZIGHlpssNJps3ryBikqapGPsFoFr6qgYyPuoMJJLTLNAiuTwsEut7HBqvs7QD9EMQdUOcc0era1bSLZ56ovnefP/6uJxlU8//iDX3+phFXKePL3EpbWESNeIdJckiekPPGxDxdQy7mP3McbWjYz+4IiDjWMKWkLYy7nxfofZgkP/jE+xXEPTVK5eucrB3jFDT3DcHlEtmfQCk6trbbp+RqokIFIGI4/NvX0OOvOcmjYg1/CGk6GmXv+Q1dUbRLGPquiYpoVQFDw/YDDo8vY777Cx0ULYs+jqWJGzWKmTZbC+tc+5h3Oef+Gn2N+7yfH2MYZjoSgW9akCJ0+eYnd7m7OnH+ThBx6amIdaaGDKjLnlc2wejoi9kCyHLIvwRh26nRYL8zPsbq5SKPfpjwIaCzW0oo67UOLO5U3m5+YoF8rsb3lc+t4b6KpNozlHLgzSbOxjnNxHHfPj8YkXckVR7jFVDMMgDEMuXrxIq9XiG9/4BsA9+GRmZoavfvWr9/6uKMo9PPy1115jb2+P06dPI6XkzTff/JEdghRNRZgWJQOGwy5be7dQFJViscjK6RVGCK5duYqtGWhSxVB0jr77KrfXNhCqOsZLdZscBcWXqEIis5Rcc0FR8EYRQRJx+cqNiXlkuopu61Q1h2q5SqU+g2I43DzYorXbQUqNOLiM4XIAACAASURBVMkYjQ7QdBPN2CZ/4FM03WmuvrvLkJy0OEPJgaXiPPnI47h3TK/VZqpSZKAFXLtzi1arw8oLP9zuLQ9HyCxmoTFFq3uMl6To4Vgn2s9NorTFtQ/f4vkvfQn9rEsruIzrnMLQyrjSIlk95sq7HyBIWfjKIk6pjGlqxHFK+yhFDxxAxyz1Jj+PbCxVLIREkfldtcC7bqAKJHlGvdHk8sVXmF2so1QqpFqBeq1CZ3ePxI+ZXmoSpfG4q2EMecHYHETJ5UeGdBPz+AjCk1JScywMxUDJE2xLI4oDVCFJkoj5uQqfPv8Q4XGLuO+T5hmOoxHFCbausticRbl6DcUwiOVY9Mk0LExVGWtN3+fGVi6XkGgMvBDbMRFKhip8inrAVMUk8rfI9AGj0S5GIafVOiJKK9QXJFqhz2FvwLxdYmm6yv6BQieSHGUZCA1NszAMwdxshYO9vYl5KCrUaxre8Yh0pHOyYbB10EJoU+xsdVndUtHVClM1h0J1mVJg8f7aKl6gsHEY0x6mbByEZEK9O4DO6fV6mKUenWHAdq5g6wGHrRafmZDHVGMawyyhag6vvf5dSlMVGjMLdPw+fnJMa3eHvDcg8ASxLqiWyrhWjRwNZ8rgqNXnb/7kc/zdb/ws/9P/+N9SrVVYODGLqmqkecTt23f41ONP4TiTIZ5YtcnJ0dwqxeYKvb0eiiIpljNUU0dKg+WlE9QbZba29xFJyN5aF9ct4YURJb1E3alhSpir2BxvXOOVP9znqS98hcb8MppmITPxr2MQ9MkW8o+68XPnzrGyssKLL74IjAv36uoqly5dIooiLly4QKFQuCeoJYS4R1d85513GI1GzM7Osre3x3e+8517uHuWZWOJ2fs8gYcXDUwESZbx+tuvQRbfdY3ZJ0Pj9tYxcawyjCQrp05y4cLj/K+/+c8pFIucO3eOtbU1Rn6fGUflS6YDaUQ2aJGmQ8rCRA6P2NMzzMpknWcll8xOz2JIgW7P8N6NHW5trdM73MHIUhbnZjEtiVBjhn2P1r7Pn/x33wTFJMbAqMywtfMqJTvjs3PHnKzoPL60jKZKZjWVwcEWU66Ja0zuyJdkwGMzS5zqqcwZBTx87hRc9l1Bd87i93aGdPojfvdf/BZTJZufeOFr/Obv/Dbg0um0MTSJknb5iec/zZZ2h6KjUpQZ+twsbtFCnt6m02rT3518U/KGIZFU0YUkQCLzFClzVMYv8kxPCaTB7ctXiI7v8MznvsABJW5f+4Cl5WW0Up1RIoh78V03qbsvAyFQVQVdxiSkKPfzvgN830cIwbMPLUEakfhDVAViw6LoGOiaRVGBsLdHGg4YdYckSQSlAtPTNdQsw2uNWCjW2Bv2xp8hl2iaij9MSDMVzZycRxDHaKbOMIopVSTNAtDfplkU1Iw2H+5tsL/ZxZnOWTgZ8+Kf3eT0/INU69NUZkNe+NkYGW4SpQNOlR6k2R3x1LzL9YOMQ2Gxd1xgP+yjaJN7csPQYRQQhxGyXKZowsqKSssbEikzvPTeSWrTJdRNSRqWOHni0zz21BHt3ojtoy793RZzi0cEd9ZIfJ/FepELDy1y5Hnksss710JGXhfTnpyH61Tp9TsUihZpHHG8v044bNOs1ul1uvydb/wdVE3jX37nGleur+NHcGr6FIbICIYDsqM2b/75S5w9s8R/+o//a3x/iM5dYb48I5YpTz7zDAdb+xPz2B/4qFJw2PVICoLmAxUG3ZRoFFOfWebW6ge89v1XaE7rPLS8RPmRB7h55yaqpqLpDZK0QZLnxLKHVnWZq+oY6pBbl/4lNy5JiMDUTJA6J//BP5uYy0fxiRbyOI4JgoBer8fKygrnzp3jypUrY9egNOWll14iyzJGoxFf/vKX79EShRBsbW1x6dIlyuUyn//85/md3/kdjo6O7rFaPop7krKTIh2RKxLHcVEEeFGKaZrj/0s1abf7KKpGpTLFI48/xZ98+8/vKRpubGwQxzGNYoFHayaP+ZI88gjVjExXsKOMIPJIvYh+b3IHemqxTOar1AoLfPvFV+mHA4omfPGFp3n2iadZbDbxoxbH3V1a7T6bW31+85tXsQsOTqGAtE0UVSBkiuqNqNmC5x59ko2dTXSvT/twhw+GXRbmJ9OpNtf32GgNMKOIX0gdLhg1TlcaGG6NdxY13pbbfOXHP8c3f/cPeeSRx+kOD3n+iQvIdMSr73bINRdVNchGEVJx6feOCQyTwfEVatMFyF3i8Bh5HwOD/igkyBU0kePnKpDetdlTUaVCrMYouUKh3kDJfdqb62wfxpSqOketXTZXdzn3bAMrDZF3tabH52G8a2AhiBn7KP6oYVgaai4RmYoiJIqiUXAsBv0+/eM+29mAuUaVKE6Io2jcOQuFQa/P/v4Rrm5hKCpxPpYrVVWVkZeRk6MokzFQ17FJ0oiZQsy8IzHiiGZdMFfX8EYhrdaIjjegIgSDXopdSnjm2Wly94gsH1Ao+HjBgGEvwrGbHO8NyHKPxBOUi/PcPs4Y+RJTnZyHzHOkHEvyCsdGCJ001hj4EWpR461XXmH5xCwyhWZjmsbUNHlk41gmrpuxslKgUZsm9AL6a7eZb1SZqxWIc5/t9SuovRHVeoM4Dyfm8dCD57hx8xqd7gGjbhdNDVHjBM0tU3WnScKco16Ho51jRt0RaZIwWzKpFkxmGg1kGmPkgv3dLketHUqlIpVCmSxNyPOE5YdPIhSFS+9f5ovPfeqH5jHqtylaDqPuMQqChdllDtI+/aBLWU3Iphw+vH6VC8oylnaEaGhc2xlbNgo1vgvDpGhZhGlXx7LZMoM8wS041GybKA8J/PbE5/Hx+EQL+XA4pN/v34M/Tpw4wXA45ObNmwgh8H2fUqnE2bNn0bSx5m8QBFy6dImDgwOeeOIJlpeXURSF0Wj0A1TFj+J+kpwwdoBJVI2SXeDpZz7DwcER7XabbrdHFGc06g2m6tM8cv4xrt64QaFU5ld/9Vc4PDxkNBqNJVH7x/hX3mKUC0rFAjVLIY98ippCkisU9RKn5WQeeUFXSQ0TbzDgaz/xPOWaw9KpGZaXV6gU63jtQzr9jCQ+YvcgJMwksw88gmoUUW0bYTqoClSyhEbsM21PI5Kcgl1C0x3W1g+oVKeQSmliHldkhh0eEiY5L4/2eWQxxo5aVKTF7bck/+jXf5W1W9f5uZ/+GwhF5cp7rzNTdOgPU37hbz3DtfUW29vbpFnGA/WHuNy5RGW6iOyPEEGFnB46Jq41uePqDmNCoSHIyJIQQ83I70oE6znEikTVDJrnHsO78R6R1+fid19n+ZE59JkZqgufIZU2/VGMSo7gLh+XHClywhRiRRsPoyfEx/Wl41xQ1A0UkaPeZY7EaYIXJxzs7xONVBbqddI0I5c5aZ4gdZUoiLl2a5fpEwuUDYdhkpLkCpmfI1RBHCbk/uSOPIl81KTH0pTG9n6XKLHp1Rx6UUQwGDHyBKlMONjJ6LZS5pqSknPIkGPah0PyYYAYWhh5CdUIqDbniLyYmSpktkRTfBTDQbuPwYVKRMG0qJc0nGKJKBKs3zlis5XSiQ85OLiDlvZRUJlrVDg+PGBzdRVNV6nNTFMul6m5Fa6XblKxdE7MNDEMB28YctjpU3OLdPdTwmSyj4AfBKRZxN7eNjW3SB77RLnH7vouz/zYT5GlKkeHbbTQZ7FcGFvlkRANBiTkkMYMkEQIrt/4kMceP49hVXEKVSolA7tUZm1zB9WcDK1kkUeSRshogO3o5F4LwmP0/JBGRSDqLut3ujhuAyFs8izh6XOnUbWxW1aKJEtilCxCWlV01UAmCagWTrHMzuo1OscHtNuTh64fj0+0kPd6PXq9HkEQUKvV8DyPT3/607RaLQ4PD5mZmeG5555jaWlp7E8J/Nmf/RmWZfHCCy9Qq9XI7rp9pGmKlBLXdfE8jyT5a9eX++mA9/sebrlGIg3m5qY5f/4JYPwiSdOEr/4bX6HZnKE/8jFtC7dYYm1tjYODA9rtNnEcM1+vo9ZPMfv04xytr7G3u00hzRjKhMbJRYq5YPY+NEgl1nn80YcpuGUqdpU0SpibncNxS8RpjO+Np+lppnPYTjjoCtzl81huAZklJIGPGQ6ZUzPOl12cco23rq9zY3OLveMOL198F101URWNX/yVf/hD89Byia0U0NWQxuICeslmt9OmbztkXp//4b//34iikIceXOFT5x/g7EKTt966xPSJFerzZ1j99uucXFrksfMP4wmdyxK290eolkLebdFLIhxDZWZxceLzSLOcTOTkecLx1k1KjoVdqoFqkCmgWmUyKagvPYg9GuD1brB4ao7jQcSDD5/EdCwCv41iWaA4jFkvH9EQBUE+NnQwxY8+ROr0fYrTRTRVhSTGsWz6YUyGilBzTMPGG3ikaYqmabgFB9UywEs47Pi4tQS3amCiMfIj8jzDLINQFaJwciGPEph2bQq2T0jMtUOFd7d1dCUkjTKWKgaGojHTqECWkQ9C3vqrWyglHdMt0KwsUW4WCft92n2TWBjUmxbzQtAaBTyxZOE0Z/nu969NzKNQcgnznBnDRC/Z3LpzQLvfZRRqHN65jSUUur0Bc80GSTRifbVL5I1dt6rVAsI0MQ2HmZkGVec8D66sIFXB0LuGl0GYQuTH2PeBVjY27rC+vkalPEXZKaMrLsGgS56a1GsLXL2xxubWJv2jHaQcW751wwBT1yDP0BUoV8rUqzUee+IRiiUXXwq8cMTNrQ1efusV8hRmZyazvIwMHLXLmcWQUtVAZscsWF1UJIsLBb7zxjHHgyG7nR6zzVl8v0vVaJMFEYmmkigGoKJpOobbxHIL+F5Mu91mb3eV67duUi0VOHF68k364/GJFnIYd8yDwQBFUXjzzTd54YUXeP7553nxxRf5mZ/5Gaanp+85/XzwwQfMzs7yxBNPYNv2vWIthGB+fp4TJ05w5swZvv3tb7O9vX1vM/R+GLkfJOy0NrHL01iuIIwSqtUq8wsncF2XztEaWZogRI437HPt2hXW76xj2/a9F8zNzV1K9TmCx56k/uAjPIyC2dpg9eYV/vm/+hYnSjUuNCdbrDUrdeoVF1VLOWytsjBzEsvSMZAoQvLqpbfp9tokQkW1plFsqNQWyAzJsLOHHrY5kQQ8asPNtQ2uXL3Oexs7HHU6xDnkigkyRFMnC+VDTpQlaKqOUHP80KNsO6Sxz2yjznvvXmJvf5OZSoXtnTuYeUrZMAj9gKvXroGeM4wDNF3l+HiAXVfJ2wn+yKJxwqV785BMCvrexsQsFHIEOTJLufHeq9iKyvTMSYRtgSlZWLqA7pRIFZv6/AmkcsgD56usHnjkosruzh30wT6VxgLV6gNI7m7jkqOQkaNiiJRm8X7PYxxCCPYODihbgoJukaQhuZIRBTlIFVUDy7YJo5jByKNcclFVlTgKUYSkXLA46B9zutFkaWqaXmedNE+JpU6USxRlcuHq94aU1Ay3aKKpJkEU0RmGmHqGLg36foJlmJSqJUhibL3Kcd/ASRzCrkAOFLqGh6MBWYlb1w4oV2uUGyVQJCXTo9vaQlEn53F5ewTYOK6OoyWk2RDdiEniEVGoYloWUZISpxn7+3toMqFWtAiziDxL8bwRvYNDHjpzirn6Y5giIwNs12U46NPvD1AQOO7kLcbdvXUURWH51MM8e/7T+N4RVz/8EMsss7Pb473L36PerOGH3nhbW9NQbROp69iGhqVrNOdmcFybtfWbDEY9dnsdgiggyxMsw0bXCswsTPa4zeM9LPuY+eURWuwTRArTTQXLqmA6Dop06I8i3rt2mwdOnUZTU4qug9QUpFsgM0uowiQIM+5sHrF1+A5DL2ZwfISua8ysPMLC7DRzzcl0zI/HJ1rIL1y4QJqmrK6ukqYpxWKRcrlMqVTi137t18iyjCtXrvDWW29RLpdZXl6m0Wjc88X8uO7KZz/7WWZmZrh48SJ7e3v34JqPjJAnRbNZY7NznTdefYknfuzzRFFMJjSEWQCrxPyjn6JWdinogicfe5gkGPKXr73F915+nU5/RHswJE8S4s0r/PZ/fg3T0Oj1uihuheWVk/zSP/0nvPLd1/mjD6/wTyfkEUYxL7/0GkkaUi2VsU0TVc1J7nQ5ODjkL/7yLzk+7lCvzaKYDQZdwdu//5sEaQ/H1jhbrzM9v4hrVvn27etcb/sMMwVVddA0hVzRMCz7vs/DtDRMzSQTkiCIqNdKWLrKcNBnd/+ASp7wDx7+Iu9Vq+Q5bLf73G4P6dzZomjq/NxXv8q/9W9+jW5vn2svfQstreIIn/NPLvL+nVewbRfVSNCpTMwji3xSoaGInO7BPhu769wSr5JISQ6U63PUZuapz53kkSmbMzMFvvMHr7PnBVQGx3hHO5iGwK1N86nn/hbF2gJxDEoeM1WyGfZ3GR2tkdZdePr0D83jo3OUZRmd3pDbGznNWolasYBiQLmoctwd0R96NOsVgjjj4LiHW3DRVAXHULENk+XFCrVzD/P+9XdJhKR0soaXJox8D6diQz65gJZtGA1D/vTlPTRzkZUZcO020wI+dVpyfbeEkDm9vT6VokGWR/T6FoMwwrZ1VMfiM59/lhPNItdfv8LiVypc2w949bLH1n7O4pxAiJx6YbLV20sfCkajISUnYmn6kNKUxcKcQyIF11e7uIUmlqFy+fYdZqaqrMxOMVuzCQKfl17/Ps35E+wf7DPbbPD017+GpsDVO5vEmUIQq3ihj6lr7O5Pph96wQG18gpJUOG9i6solopWPEciFTb3BihumVaUIRaWiaVAU1WEAmmectzv0DvY5fVr77NYVnEcjeGwhx6PMKQgTQTtds52t8PqZpd//O/91A//Xly4ffUGV66nHB8peLHgTstjp+WhJikXVpr8zN/8On/xl6/xR3/6Kn//7/0Ca70eazducTA6YL/VoXPUoz1MkJrJieUqTzz5GI888wVKxWlanT7V6Qa3trb4ysQn8tfxiRbySqXCwsICZ8+eJY5jVlZWKJfLWJaFEIIXX3yR/f19Hn74YWzb5sMPP6TX6/HLv/zL42Tv4qVCCN5//32klNy4ceOvbd/uFvv7deRJ5OGUi7Q32kRdj6GioRkmQtVQdYPa3BRRIrFNk/rcCZQ85avlOl947gu8/OobvPhXf0Wv00aGks7RPrPTdZYW52gNPK5dfo8LZ1dYOnWa9Y3JtK6Ll95ja30TbzjgG1//GrZhYqg5o6DDsHdAPBiQDkdg+YTDFvt3jokjDyEg9GJuBAd0Oh5z9TodYwqlYCJGPhlibOIsFUy7cH86pqqO+9W7n79QLKDkIbOzDZrSQNk/5J1mERMVzwuJsozp5hRh7KPoJpatsba/ysgXTE+XuHLjmOXqIo5qcKJ5njvyDiI3mJ2b3HH12y0y3cSydeq1Jn5rmzjok8Y5JAqHo1u0D7c43Fpj+pEzfOHxZ7l9c43EFfS8PeiHVByXcDhi49pF6rNtRiPQZAzNGrWChq6GxH1/Yh7AvZtdJgRBlND3QnRN4Og6qshR1RyhjL084zRF0XRM20bXNGSWMgo8yq7BXLVKdPYh3r11+a4zeo6uGGPKqpg8dA0Cn0To9LM6U6qHyARZIpGqwLZ1bmxGHOwdYCgJU1MmtbrO6VkToSnEccCtG30+eL+H7Qqa9dPM1VIemrcwsow/DYZsHGkU7JzCfTrhYsGh742IhEXfD5CaT5qDoUpKRYvRaEh1YYY4h732gIJtszJXwyoUONjewBc63nCAYxsoMsUbBVy9epXN7V0ioZMmGZqm4Lj3waZlCc2okaLg2w6mZSKkikBHNXWEqSMZL4YpWYjv9Tne3yINw7tzLJNCpUE4bPPB5Q9QFImtq2M5DcWhWCqiDHzi+0BvczM2+7dDDg8jLr7fJco01OI0hWodLU3Y3tnlyodXePLxC3z/tXf5n/+P38FSVTY3thkJWD79AF46wssznn3iizSmXRRZwVBqyCTBkgnrN64wNTU9MY+PxydayNfX16nX6ziOc2/R5yPa4MbGBkEQ8OSTTzIYDLh8+TKHh4c89dRTvPfee6ysrDA7O3uvI9/Y2GA0GgH8P/wxP85i+X+LfHuPOI1BCPb29li0VSzXxgxGjEYDRl5AuVhCqBZeJCk5BWaaGsViwM/87a/x7LOf4rd/67d46S9eZGl+Hl2FJAxRyalVSgxGHlEoaQ0nT+Fv3Vnn5rVbTE9N8+Uf/wrzszW67V12t26zvrqKoYyv9LkXEoYRMhiiGy55EkEuiaVkcxixHexTdVyiVKAaJggFxy2SZBLNNBmNJruC246DoozFqZI8oz/waE7XKJfLtPc3KT56lg3NYMk0OfvgWVa3b9NrHVGrlMZGw1aOF3RoVB/A9SwyKTh9dpn5EzXMA4sbt6+QBCqt8mhiHoc3vo9TLKIUCtRcwbBSppvHhKFHEMbkhoKRpES9Fvlomt3b+3T7XfZWD8mRkAiqrk2lXEAGL2MY75DlKqWCQ7dS5olnLkCekMT30XwRH/0RKIpKLgSplPhhQp+MZtXGsVXiSBAmOTJNMG0LoY6FvaLQJwgDpuoVjnb3WHngBJ1hh5sb2xQKLsIySGVAkk/W9IhznVzRsDQV187QDJeNvZChGZBqLsedgE4/ATIOej7GNty85YNUCPyYKJKkjH0j5x6wOb/k8B98vcrKdM6pBY3Wah2p5AyiyYXLthJSGaCYZWIE7c4AwzTRVY3pqSpXb7QwDUEqYeTHrO4csDJbJlM0jrp9OsFYdVHTdHZ2NtnbP2Rnbx8/DIlIMVSJ7/sgJ79QVlaepFaZR8GCggNCRZOgCJU4hTTsk3sJQadFkgyR0qdRtSlVS1iWRa/fZ/PWLrsHh2g6mKaG0FTyDAxF48lzD9LPIrr3YaeGw4Dp6iw3149JVJhq1JmZm8ctFlE1ne2bkr2DQ04tP0ipUGBjc5tHlk9QrU3R6w5IFIMghUq9ScGtsL+9y0HWJo8yFhdqOKqkaBgsNJuTE/lYfKKFXErJ2toalUqFYrGIEIJ+v8/m5iYbGxvMz89z7do19vb2CIIAVVW5fv06QRAwMzNzDz5RVZUoijBNE9u26Xa7/1rMlcVuQMsWdIMhXb8H2zmoAsN2iOKA0Au4ePEtzj74AI+eO0sic0qFEsVKjeFwyBnL5j/5j/8jFppN/uj3fxdNSBYXF/nxTz2NVCSbGxsEXo436EzMY/fOAU9f+DS//As/j2tq7O9u0Wrtomkmc7ML/Py5FfwwZe3aGrc+XOOQGFu/a/ScZUgVpK6QiZzRaDh25JYSVRXEUYrluKRRQn6fwjXTrHPc7iFlTuCNsC0bL4gIwmO6BwfEs0tYiYaiCob+kLLjcKvdRSgalqWzdzDA1qqcmcloH+xgxBGL83V0ETOId/jiY1/g2997heW5ycPO3t4dfMNgaFpoUjJVKpD6Hn7fJ88THKuEauhoKmRpwpUPr2NaOopUSML0LkNFozHVxMoFehKiazpmIgjaEe+//TaFUgnTmIyBaopE1zWklNi6hqEJVCUfq0qaFq5rI2TGYZRwZf2I6YpD1VBIyIklZMEIKSShpjOIchqe5MmVR9k/7hLkPmmuESsRsUgm5hEkKUJkaDLDsSu0u0NMXTCKEgIvY3FakI4EQrcYBTlxlDBSJbqSYtg6hZJBGMRomoGqKVy/c8jbl2M+92iTKdfGtTRKVZ2D48mQRqbaDIM2YuBRa2TkQifOBYpmkOY5Qh1vwWqaiiEUfJnz/atrFIoWwhCEnseJxgqN+hR//tL3KJUb1OfmsTf2MGRKpWQwGnl43uSb0qn5JVTFJAozksTD93wiLyBNYjISyhWHLI3RFIVyxcV2y/S7x+x09vB8n26ngxeERElGoWiMN3jzDPKMwBvQ67boHh+yN5icR54mCCkJI5XS9BKnTi1RNVOKDiSmgTx9httXL/L+hx9QLDjUqkv85I8/zdU7R7Q/vEGYpARxytLMCdpHO+TJISLPuXHbB+1hHlxocOLUGcLJx+MH4hMt5JZl3Su6CwsLCCEYDof3pG1ff/11RqMRhUKBSqVCmqYEQcD58+c5derUD2Dkjz/+OKdOnULTNL75zW/+gJ75/eKklzLUDYxamV7JJIwCtrY3yRBYrsPJPOFP/uD3+V/29/nVf+dX+fpPf50gV9ESSbEyRZZEJHHCP/wP/xHr62v85FeeZ393lzfeu8zciTma9SluHq5yanbysNMVFn/jC89xdmmG3Y3rlKolms0Z8vI0vW6XKcvCnKowX7Ap7HXp7exwK4kRQgdNRagKOWN39kw3QaaocrzgEAx98iT/kTZd/TAkyVIUUgSSYsEiTyMUmZHGPkkYIxWJ5wds7e6QJx5zJ5bp760yN1ujsTjNyQdPYRZsPmy/zXTdZRjuctBt4QY1lELM8196hLnqZDaAiiD0fELPwzENLCRlyyAvuZiKglMpECYJqpB4UUAceZQcl/lmk8OD1li0CHAKLq6hIrKMLIpwXBdN07lzfZWlk0vMzs5MzMMwNFR1LIhmGDq2reO6JtVahZJloak5pYJDP0hYP9hHZil22WQ0yggjMBVBpltcW98lVYqU3RLLpxb43OPP8Jdvfg9FA0VVuO8GdjZWfBSKwsH+PsVimenpAvt7AeEo58nzFQzlkM3dnFAFRVcoFXSm6xqaIggDSWwICo7BE88skocN+kGHjh9ScV0U4WMYLmk0+UV/3FNQ1AKWpVOuqBwdJ6QJhHHEccsDoRDHCSDQDBNVUxklEZrMqDer9FojGtMVpuouq7f2sQolao1ZTE1lOPJwnRJRFCPEfc5pp4+UEs/zyFUFshyRSwwyDFuiZwFKnpLECj0/Z2/Hp9vtgxCkSYrne4RhTJJLpJKhGQZmLlHJObF0En/URRMpvdbkhaBCMcd3U4SVsjQ3S8k1mC1I5uoW/VwBZZbDvVl63T5O3cA2BI6eUrAVJkf9WQAAIABJREFUGtUSwywDXaVcLVMVI+amHIIQbh+kdAdDSrWHMN0yw+5kiY+PxydayF3Xxfd9PM+7RyG8ePEi7Xabfr9PkiSUy2UKhcK9VX5VVZmfn+fdd9/lwoULGIaBlJIvfvGLaJrG/v7+PXOJjw88J0UdwdIwZKpa5qprEioWfhSzs7NNKiUriwtUXJuLq7f5L//ZP6V1cMCv/cav41oGQZJiGzZ+mLKzs8tv/JP/jG67xX/xX/039CLJv//sZ9i7+ha2mjHlTmZHNE+VMUo5azvXKDs6miLxhn16/Zi+H9HrDtD6XSwvZMExOFe2+YuDNrlm3V00l+QIdNPA1A0URSdNEuI4JpcSXddRFIVBf/JiUqgFoAiyRKVUNCCJqBQdCraNElbxkbiOhaqqmLZDZ9Tlp3/p32V79QMatQJPPfcChxv7/Oaf/yF6sUK9OEPsR+zsbvLQyqM0jWlcq4SiTuazO44JjBezDJmRGypkDpauUipG5LqCFwIKDGOfzI/w+n1OLS4wM9Xg/Q8uMxwNOO51qUw3QOZkaYyuKeRZTBQktA6PmapNHrqals5HIi2qIVB1Fc00sIoFNE0HEaBr49lA0TVQkRz3UvZb60Sxz+OPneba5j4frB/hOgHFQgFVKDz4wBnkU1/i2++9hkxiRDb5nJYK1njrUAqQglNLC9zZOGRmdg6zmPDAgsXh7g12dhOqFQPLMImilDhKkZrAsg3KZRcVSdmR1BoWM6VFPHqUqyX80Q66JWjUJn8vvUFKkmTEUUAYCbLcIJMqQqjkeUSWxYw8jyjKyIRAN1VUAVJRcUolvGFClEcEcZ9KRSOOeuhKRr1YYNjv02l7dPs+WT7592Kgomg5RkEnihPCJEDmY3kM3/cY+R3SLCWMcrIEBDp9L0a3LCQSoRroloY3HI2VTeMELYdqaawk6fsBJxZm2DmarNJZKNcYuBVC2WOuUqDuqpyasylqHjW9wk5PUKou0PaSsRSBomM7Fqapo0mJNxyQK5Jcppw5YXFqOqYzshhJk86gRRyHJL02tj15ge7j8YkWctseMyg2NzdRVZU33niDTqfDcDi8J11br9fRdZ1yuUyapmxtbXH58mWCIGB+fp6FhbFmyObmJu+88w63bt0iTdN7cMpHw9BJMe045NEI4QXMHQxZf6DKzfkyTmqijXz++A9/j067y4XzZ0mSlH/xf/7v/PQv/V1ymWFZOrVamcNWl+9//xK940MUmdGcPcnTZ1bIfQ8Z+cSRj6ZNzuMXf/6nqJZtep1DGvPnuXR7k43tA+YbNWYXZvCGOWkUs5DBQq4wPTvPB8USr3xwlShOkVJFKDp5DtIcGyx8XMI3SRJM06RSnXwz2Nq8xuLKMpEdc3TNQ3UtylYNL4q4fTykWS/iDfukWYzvKYg84srbr/K5555j9uQZ3njzIt99+Vu8cef7JNY+9c+Y1AsXePQRhcGBpHlmiT6rNCu1iXlEZk6hUMAwTdw0QggFq6zjBwGVPEWxDLIcVMVAJedw84BmtcaTjzzM/t4RmRew0+9w9cZ1VsoVNF3Q8joYvsWZ02dYe/N9rEGHqaXJ4l2qqY7PlMwJRUgex3T3O2zu71Gv1vjM6QVc08a2QxbqFXQp6CcxU4UKjaVpXnznA24fHBOpJlE65OrOHTZ3d1nfOaTenOWnv/qz/Nn3vsVxbzL0pmk65ClZEjPTnOP9q+sE8VjEaq1n8+GdDmeXzrAwk/DW9S36XkLRNGjUq5TLBdY37kAiWD55ilp5jlZrQBhlFJxFPrh9xPT8AkdtH0WZjNX/+m/8Cnt7hyi54Oa1awyTXbxunzCMMG2V84unKLkWV1c36A09hOKjKiaddo/RKCQLQebQ6bTQ7Yw4HbK6+gFf/txT/Pn3Ylb3umTSRNxn/6NztMuJEzPstQ+5fmuTJM/IZM7eziF5oFIq2wSRhx+1qVanyFOVCAOpqZQrFaIwZDT0ieKUknRozJZZmJujdXDA6PYaQTDEi2PCcPJs65W39pCpThha6IbD0lKRhfkeZu4jcovynouua1i2S+KnHBwl/PHrqyRSp9Xx2d/do95okvmg20UK1RJ6zcHoDUiHCXHQQ0hJPJo86/uBs/Ij/8v/j0JKSa/Xo91us76+Tp7n6LqOpmkUi0Xq9TqFQgHTNEnTlKOjIxRFwXEcRqPRPTbBiy++SL/f/wGhrB8VWrGFiqqN5VIf9DL8bp/DxiymYVEyHHZGPaL0aCxuJHMs22Rz7TanTq8ghWDoh8RpRr/Xo1oqYRk60406Ub/N1YM1zDxg/6hN/T6d30Jjhij0MTSbMFfYbLXZHfaxChqVtIRjOzi6zaytklaPGAUhP3bhLBv9Lqu31xGMlxSlUMZcaUVgGDp5rpHnGVmW4vspljWZXmaGJr2gS22uyB7HbLdDRmlABZtEgurqiFxBVXRUVcUt2rxz6SK727uU67PEes7a7V16u0f8vb//twkLR0groZnMolu7vH/rLUpNhVC7wucm5DGKI0IvQ01UyjJHKAq5CsI1MTILoanoyphWqQmYmp8l7kZYpkGSBGRZNrbIszXSPIRMw1AsbMMmjBJ6oyENt4ThTqb9JWk8FtxSBEK/q8ioqeS5oD0YkEvIpWA0jCDPcWyTqZrGyWaJgg5hrc7C1DReEtOPQvww5HDQJtAEcwq03x0SBwnZfYaMlmkhhcJR64gkzTA0lQyNPNdod2KEUuXGVgd/0EJKnVJxbKjhDX2GfY/IV1EtjeEg4/2bXVAiThUL3L52xCCp0Y09FNVGsyZ3fp96/AmS8zEI+PyXP0schog8I4kSNjb36PeGZMGIL73wWfZaRwglY29nDy8IyTKV470elZKGocekmYGfZnijHv1hB0UD3TAglWRyMsRTa1RIZcrC4gm6gcK7H7yPF4ekaORSwU8kUQJSsWl1hshcIIQFuoHXG4Kqkqc5mlMYy85mcHDc47gzxA88VHV8o4izyRCP7jYgK/LcZ5c58BTCQAdlCtvVSHyHKAqZmZnm6ccfQckSCqaBtC1A4Gewsb1NEkUM4j7XdiV2YQk/1Vnb3mCqVqFccjE1Fe0+Gjgfj0982JmmKVmW8a1vfYter4fv+1SrVSqVCsvLyxSLxXvFPY5jarXavS3OKBp3DkII2u32DxXIul9B11AwhYJEYCmSqfaABwZ1hnWDyFApZDaObY1digTYlskf//Ef8XM//28zNd1A01S8kYfrOhiGyurtW9y8eYNSwWSxWSP1EkxDx7YmD9UUITB1i9jIuXL1Ktv72xwN+2AkeKR89ekvM2dVsI6HtOpl1GDIp8+fZbN7xM7OOkEcI/Oxkl6aCoTQ7r7ogLvduRAKcTy5wxA1G5nm7N/cwp5SKBYKDIIe7XaLNJEknqBQc0DVcdwCeRpy4sQCuqZQKqm8+t5t0jBhurzAs088ynHQoj/sUnCmOX/+GV5/47usH27i31H5xQsTvhdVBTH+QftpBqpCrggUVBSh3XNwkqSkucQs2ERpzOHRPp1eizCK8MOQmbkp/HCErttUS1PIOOdgv0WcJCiKRNcnd35CYfwSkfkYwhIg5JjKEmfjQZVrQOCF6KqGbhpUzYgpVydLM5olF0XTyVHAtRnGAautY/YHQ9b313H7NgEJUk7++aVZSiRVVLtEjoLr2ESjjDBOUXJBqVql16kRywDTzMjy7C4xoMFwOEJRLVRFUCjXGGUqSqqyujZE00voqobQq1imSZJMPh9TtSY5CbmSItW7k80sQUg4eeYhkkTg91u4xQoZOXkeMOgfMvIDFGHzxstvs7e9xuMXnmLod+j2PI7222RhwMPnTqIVO3S6fcJ48op+lARkUuXEyYdIzWmub+7Qb7cwyzapGoMhyFMolaaJo5DAC7BLVVJUBBJN10EoKLqJYQlanS5D4WGaBm7ZQjMyRn6Ieh9HhwfOPgpZgqPY/Ku/epvjjslht0AQacSBRn/YYTDqkwRdQm9AHidkQmM46nPYOsLQIYkCDg63ULU5ukcJCQZ+EnNyTqXbPcbQDHT9/6eFfDQa3YNRdnd3URQFTdOwbRvLstA07R4XPEkSkiTBtm2EEBwdHXF0dESapui6fs/j8+PDvB+1K1eFwMgVUBX0JGHJB22rw361QatoUshspiplev0eMpcImXPprbep16f4sc9/jvr0NL1el2KxwMXXXiYKPFZWTmEbClVHJ9NS+sPxAZkUYdBDUXQ6/Q6bu0cM+iG9joccRWjDGOuFEmahgpLkaK6NpRs47WOeWGjy9kKdD9a3SRUTNQclhERJyBirRaZpem/WEIaTr85ZPcQtpXiDFNnK2Tvew5qyyIsSq2wwjEYouYqaJMg8x9QgjASq6rC138WLjjj50DTCrqKVBEpXo1JsstVucex1OXW6ydblDbzeZAy0qBtIVaLqCroEqSpkQpLKHKEr6JqGAHSpIPIcXVFIs4iDo0M8PyCMfDzPp32sYDYqGJpJimTojwjTsTuPaWrje/6EMAydJM/J7ppxK2LsLJTkkhwFL86wo5QsB8O0yBUDIUL8XAHTBiLyXJJLBV1RWZyZpjHToONH7PeGrO3sEPg5YTy5YOh2kX67g2ObuMUS3aMuUldwdQtVKDi2S1A6gZrnWPkxUeIjdR0hcv5v9t4sRrMkPc97IuKs/77lnlWVtW9dvcx09zR79s0a0hyKoCxRtEFDtuH1yoZh2BcW4AsJhmDBBm1YkAyZsgWDsgVQECWKFLchNZyZ7p6l9626lqys3DP/fTn7ifDFyaopUuzMoi/aJFDvRSXyr8yT8Z8/4jsR3/d+71uueOSjiFZzjlJtHtdzcamgZI4RklmYUm02WZirsXlv/fj5oSyMkSDsQoYWg5CaTOdIp0QQZVTnV8DY1EsVkmREpeqTJTnVcpODrS73793m+rOfodZwEUgO9w9Yv32HlbXzbOzts7GxweQEPfJut0u73SFJcvb3tmm1moS5xrE9AiZ4FRetJH61iZsl2PaIxaUOQilKpYJ3blkOQha0Sc9zcG2XMJxRLpc5ONzlo9t30dHx4wAIk5Tx4S5azxhMFXe3bGpVlyQ3bO8esLl5G8kMJTKkEVT8KpYjaTZ8Ws0mOreJsxyTJmRZinAMC+0SraoPGFAKYT+eXyf8/2C+HIYhURTR6XQQQlAul/E878fV6COd8iRJyLKMXq+HEIIwDAmCgDRNH6Zk4McCR48G8BMbYITAkaooCBrNfJJhumOmBz3yZpO+Y9NqNbl79w7z8/NkWUYUznj7zTcYDPtcunqVdrPBvfU7pFmCbStKfh1XGiwSmu0mWzv7J+7IbWWYxQFxnrCzt8/Ofo80M7QbLZ45f5lStYKxBKZkY6oemasYrN+i02nwwrVLbPW67A8jBDYmy5CuhVDWQ3MNY8zRw+74gFEr+biuoeZWyOsBTk0w2x9RO1dB1ATkU8bDjErapB9OEbkgispUyhm26xJNIsLGjGtPX+Y3fvtbjHszjCfYPxiRo/nGF57l6tLTvNK9e+w4ykKidYaLJM00OtcYNC6aslTIVKOULAppQpBnKfOLbXSq6czP0+uPsWLI8pRJmKCtlDxPIAlwS2U6rRqrK4scvx8vdMMFBksIbMtCCUGW5SBBWpJhHJKmKXGuqTgelrJQVpnBzKA8gRIuRhik5ZMLj2lgUMriVLvD5XN1Ll+4xju3Nnj19TeOHUemc0qeTb1cRkqwHZfZbIZfKjjcWifYtoddqhB1t6i36sRxzHSaY1klhHApV8+ztHyF3ijgyoUL7OzfYTKFPFUsdSqsLDbod49PvR2MpwhRsHkcx8KWElAgwLIcdnY2uHppFZ3lCClQyiKN5RGfPWV/f5+FhXmq9SY4NgZJa6VBZ/UKjpNz7soqUfQUJxl+eE6J+c4C7WaHF56t0Gw3+NZ3f4AQNnW/wjiN8EONdKtk+YTpaEL1dJudnbv81f/oP2ZpaQnPc5FCUnV9yr5PahJufnSTra0ttkeHtFxFuXO8U2Y4nTKdzIiGA4QJGY0ixuMe5UqFVGfoLODc6UWaTY+yp3Bdj6pXwvEsHM/D4KBTizwR4OQ0/TpO2caruNRLJaq+jxHWUcPZ4+ETDeT9fp/9/X0sy+Ly5csPWSyTyYQgCLDtYscWx/FDxkWtVmN7extjDN1ul1/6pV96qFP+IGA/YKw8LoZJSH7E/cq0wdiaajjl9PffheGE6k99kSyZp+zZ/M7v/C7D0ZBnXvoSq0vzSJNx++Z73DYakWtazSo6TxFoSkoQDSd05lrkSUC1djwbYONOlzdvfsAbN9/n9NXLnD87RzidsXOwzm9+a4cb56+SVepkacR0pcx08QIvPPsS6SRi5dJVPn3hOt/63it8+6032ZAhDlV8q0wcRTiORRyH5FmOZR8/Ib786UUO93qYak61c4Mf3d7Eq1cIo5xkZ0iuDlk4t8xu7z3q7Tb3b+6z1j+FVgtM9nu4wsdzLA4OXmenW2LU36TWdLi/N2XWHdNavMxr/+J3GeQT+G8+fhxzloMtLFwUcSkjx5BqcISFZ8BzHBCFP6ZbLmF5Dp4teefddd568z2uX7/ImVPz7O3usdWdsL6/xXQ04caFNZ597jnOXjnL8koLpY6f9rnIcG2FlDaGHGVJhLSQgBaGD/fuYxuL1eYqo0yQjWdEec7WRpdqswM6pz8+INGGTmORWqVEyXbxrX3qfonltfO8fPUFvviZrxw7DqMsgkwQ9adFCi2KqSmLsrIRKIbhlJLtEBqLSmeZ1KQ41SqqnlEvt0lzwySQbGwcUCp7vP/+m9QbFpfOL6KNxRtvvsveTp3dg+PlUl+/eQtjNEEwJZhNmKvXuLC2SrvRAEbYtmY2GyKwCJMp1VqFRNuEccJk1OX5z32Ber2EYxscyybNIMmgUmtg8pA8kTiWd6KRwvLiPAf7u+ztbLHbO2AcZIhME0cToiAkylLyMIYoIoymCFJu39nH9+b55V/+9aLrmxTbTWhXa9QqVYQtqVQrrK6ucuPGswhhOOlM3yhXKTsldLPB8sU1jLGwrCPKqpCUvZcwOjsiXkikFA//Tx4ZlghZpAs50tov0sTFX37gwfC4NT/4hAO5ZVkP0yLT6fRhGgVgNiuONw++f6AmVyqVqFQqTCYTHMd56On5x9/kn7Qz/ziEWYqwFVJKdK6IFRiRsxwJxr0RhzrD91wuX7qE6zi88cYbXLl8CWVbBW9bCIwGx7IwJqNU8qiWy/hCsz8bkMSFyp1tHR9Ad2cDlOextnKWcm6xXGthNVuMyxXQhnDWw1jgZoYwCEGnOKUOedRHypzVpQpf+dwzyJLgV771Q9ARmdbYlkQpiKKosC7OTrgnKqVcbhZaJ9MpTlkTGSjXSsR7U/rdEeauwGk6WIOYi2eXMXnGyD5kOJrg5hZe5zw/uHWXjhXTHwwZTxVuuUEsJ6zf3Gbw4YTW9eNZK1IUdMFUQ64KU7YsTdBSoZVNTspsMkVpKJHTri6wsrpMtTHPjaefptGssDDXIEkiBrOYw+6MjfUtHJNx/emnOezvsrDYJjmps1MJpBRYUgJFaqfg62fFAzI1IASTMAVpYXmSMBkxIGGvu0MaBZTrCrvksT/tsT3cxVYWdb9Cu9LAabcQ4wB/Wj92HJNJiBA2JV9iNChHIYxGSfA8h2kksIXAeFWM8LCVw2QyRnmGNCnhOorcsQmCEEOO68CgFxPOdnAch0mQkOkBnRMMUG5cu0aWp0ARzE1WSPGGUQomJc1iJkFIposAlUlJmIT0ewMcy2d5YRGdJ8zSAGkJjATLkXieTRKlGKsEj+G1WxADJNKxWV5coJ0JYg1RmJBGMdNwhshBJJBkdYxaREgbhVekcZVC2QJjRYgkJ4wTFA7JcMJ4eoul+RaNSgnfP/6EUkiFKEChlCpqOEdxSymFY6uHncHaFO9LCuvo/YmHnegPfueB+5nWhjzX2Jb10OLwcfGJ58ibzSZzc3NUKpWHJhLlcvlhfrtUKiGlJI7jwiw2SR5yzl3XffjagyfWo3nxR187DkaAMgKlIRYaKzekIiVNc1JLYGmNhcayJNcvn+f8mWV645BEC/KCK4I0AqkT5tslyr6Poxy2tjYp+Q7GaHIKe7HjMJ7NuHL1GnPNeRq1Gu1WDZ3HjLtdDvb3CcIpPW3IRjN6/T5Wq4ZTqpCnCdV2jTSuYLmKLyibH32ww+EoZBqEgDxKp2ScdFwFKGubWqeE67X4sKcpSahXNL1EMtkc0ji/gGdbTA/7UK8SJ4ZRb5dWYx7tx+yNdrjbbVPO4M72OjpXzLVtxv2Q+YuX6e11OfMTl+n3Do8dR2aZosAoDLHgyLXJIs0hTnIikxIkOY5lkecGJgGtpQUqtRy/7NBoNHAcj0kQkKURy/Md5hod0jim1mqSCYOQDuXK8dPesiVkGozA8xyCJEJaEkc4OFax4ISxGE4mKM9CmZxcxCROTi4svGoV5WcIqzg9CEcSk9NNJ/QOZqiKB7HEOiEHqpMEjMD2LUp+mzgKcWyHKJqhsxlCJKwsLjIYW8RaI4wFQYayHOJYc/3qaSZhwN17u0zGCYmvCMOUYNan3vAJU0O1XiIOj288yaMQKQEBnu2Aa2FLEDpD5znVeh1p24y6XbZ2d3H9CqtrS2hbopXk/t4+SoKyFKlwwGiUNCSDIegEJfRDm+zj8JWvfAkogl6YxCSZIUeQphlxGOIeFQdlJkjSmDiLCZMIQdFnkucaIQVYkqrjoaTCSIFUqqA8V318t3h4HwelFFIKQOI4onh4CJCyeN0gyHNTpOiEQgpZNPEZgz4iIxRxiiNXM4kxHGUajuLZY6i4Pgrxp/nhJ3iCJ3iCJ/izh5PqPk/wBE/wBE/wZxxPAvkTPMETPMGfczwJ5E/wBE/wBH/O8SSQP8ETPMET/DnHk0D+BE/wBE/w5xyfKP3wv/hrP2OUZXBsVegWC5C5RlPwdwQCqYo2bG0MEo2WAm1EQQgSFpZVwi/X6CydouS1mU1zdvcO6HU30ckOioLb+j/877/5sWymb37zm+bWrVt/or+nMAX5yEiByAW2UaiSj+07xXhNob2RZYYkTkmDMUJCLv8o+8cYg+/7vPXWWx87jv/2r/8No5SFMZosz1GOTZyljMIZ5Wad5c4ylgDftWjWqiRxyMHODrv7XSzXZzoLyLIc17ExcYqSEktJLKvounsgcaCV4G/8d3/9Y8dx/mLLhGFEHMRU3DJKFN1o0vV49lNf5OXP/zzzi02kiDjc3WU2jvjqV7+MkIIkLjSzR6MRtVqNPE6ZTqZIKSmXywhR9AgMBkPSNOWv/Lvf+NhxLHzz3zKe74NKCIVCaxtDgm9lfOXqdcQs59W9bULjEk5jsjSm5Ara7QbT8RAtEsqVCsuyxGfOXeW76+/THQ/w/TIGONdyuLjUoL/b5e/893/vY8fxb/7lnzXlkkXZ9WjWXHzXZef+Jv3tXc6d6vDKW7eRvs8v/uJP0aiWyHPN3/yb/4BwMuHf/6mX2d485MPNHtM4ZGc4I8lzEJCbo74J/WNJicEk+Nhx/Kd/Yc6EIdzfMUyzlOdP21xuBFQ92Itd1gODFUq2D1PcVoWLF2ps3t4lGEGt6lNvCPJMkySGROaECbhSstYq1t7tw4z9cYJC8Bvv9T92HH/1r/2s8Us2a2ev8IXPfwXbdgrusxIoqXAshZQW01nI//RLf5Mfvf5DBrsBlYaL6ztYjqR/EGIpwakLTZIkx1Y2WuWUaxZRoEHA6vJZfvWYdfv++o7RWiOVQun8EVcwUzTyyEJD/gEd2QDmY/T4j9NoUkpxdnXpY8fx934bE5kUpGF3fYtpdwfiKUl0SBJusFiZ8NLLn2cc5BxOEixxmdsfvkaUCmzHEPTvM+tu0movEsZ9puNd2nPLKKtMEEsatTJSuUyDnL//dz9+3T6KTzSQr6yukGVTlDCklkLmOVZWcG8BBAphFLrgzSMQuH4JoWxsx8cr13D8JpZTZX93zJ2PNhiNhiR5j5KradUcpLFPbElP05Q4jv/1piJAGtACZkqz1Fzk+rkrJIstmn4bmRXk/sQSJDrGxBF3Xn+dnc17yDTj0W6sP24/9ychw1CrV3Edh0q5hG0rojRBC0G91aC712Nna5s0jhBao7OM06fm6Xf3WVha5eK5NTY2t49a8jVZmpJnBoRzxHWVhai+Od5qRGcZQht810MhqZRLKGGjlUMQJCwtr2A7mvv315mM+ugE+sMBruvg+z6lchn3yHd1lo3xfZfJdIKTWfiej+e7lGKPPD9Jb3rKim1z5ewKP7x9nwCH1K4SmjGTWciiKBFnMa5XwnYF5y6ssb31ESIdstguMw0ESZhglWt4nkueJygFtiWxlIU7i+nEglO141UplZIoaRUNJJbNxt1Nuve3uLw2j++VGcxi/oN/55t4tsUsiJEKPv3CVV599S0GoxlSKSwMtlKkeYYxkkKhRKI50m7Jc05qZRSqwv5owq2tEdfOuqw1bJYaHhU3w43hykqDi6er/Gh9zO/fmuG15mmdcei/v05/v0dpYDHfrNCoeFS9hDQrXISi3MXkOVKneDLHPv5jQabgGItWvcadOzfx7RLSUoXMrizEzgTyyKFJgTG4nsV0mOJ4NlIaKlWLYJow6ocIoWg3y4UuTJKzfn8X21YsLI6PHccD5VOd58iHonCFbrwQBkkhdvYgSD+Oqcofv/6jXz/250jIpQBSqpUa0WRAlmfU6h16k81i3W5ssrlzn+Xzl3nm6Tpu2OCwNyDNYmI/Ry+30U6ZOBmjpGI2mzCedmm0l5iOEkaTCOUc72H6KD7RQP6Nn/0F7tx8m93NOzhJhvYcjO+DspBKUi/VqHgV/FoVr1rGc33mOitYjoO0fbq9Mb/1W3/Ae+/9kIXWlGZdsDiXg5AIbWPiooNLihNm5iMwRwYCD0O6EOS24vkvf5bu9j63oiFiaZXf/Wf/hFk4w1maQ7dKKFy8WPL0s9f4dFn6AAAgAElEQVRYPT3Hzh++ghTmqJXg8bqyWo0yniMplRxkFqFjzc72Hrdu36ZeKVFuSjzLQ6EIkpw4N/S6PT7z7HNs7e1x6+6HOI5PySmRCwPGOTKxdbCOrMps2yY3x3eYPnPtBrb0SZOMzCRoBJZbYeX0VV588asgMtbvrWMrydlz5yj7ZWKdk6cpmRDkUiIlhOGMVr2KTh3ub9+jUi9Ta1aZTKYsVZZI0uPNhpcaPj937TLfuHiNb88vs7E94rtb95nVXBY7FdqJ4tqpeWq1FvFogKDP8zfOcjCY0YsCrLKNCQzb4xE1BHk8wKgcTQWTWlzuVPnSmWt8cP94U2zbkliWRCr48Idvc36hw+qVNXqjMa++/Q7/yX/2V0jijN/9gzfZ3Tlk/d4upbJNlMLf/fXvstLwaVcbrB8OSLQGocFQBDsjyUz+WFPkw4OQO/sJvVxwOAZr2ecHHyUsNhSdZko+7HOoBsyR8rU5sLq3cEQJsVjmwgtLPHPWZ3Nnyq99t0tnroUtYtLUEGcCaWa8cG0OQ3Dievlbf+uX8H2f3rDLB3dv89Nf/imyNMayPe5ufkSsJbK2wG53h2q5zqAfoCNDuebg+OBXFSuX6khbkcYCD8U5u81CdRmnUuGdt3+FIBfceX/rxHvy4AQtxSPd3FIgHhGLK073/9/wOIHf5RZe9Rpkml0lKdea1FdPs3f/Jl55nhdfuMRo2OWlG58hUYJv/97/Rb835Y33PkApm/mqS60kqVdtag2H8chwevEszSQjyjI686e50JjDKx/vI/AoPtFA3ppfJpwFhbzkYExt6TRBtUSr3cH3fBrlGlmUUmk1sEs+xmT0egPC0ZC5hRVWTq/yuc+9wPJii2n3HXQyQZoUgwUStJ8jsTD68d+WEeIonVI80Y2B009dZziasn9vk7Ofe5719VtUyi1EpcZEJziRwbIkme/y4fo9zl87j13yyYMJQskT5UkfIA7HmDzCs6FZrjDoDrj10UccDoc4rqKJwkQJlvRpVMqklo2tJKA5tbyIM+yhLAdH2cRxodfgujZ+yca27YenDvsEXWMHQcWtITxJoMb4jQ7l+hKLS9dpLKwQTEdUa2WEzsm0JDPgWRWkkijpoLWFlILBYEo4ndJuNYnShPF0TKVWQwvwXJf8hI45yxY4vkW7WuEvPvcitxeH7EcR78126PW6fO4nPktv0zCYhBghSEOLUXeGa+UInTEKi/RBog2eV2a+2mE4OGScRpSN5ss/+VmsPGPvBC/V4jRowGiWahVaNZ/BLGRjr8/9vQH/6//y/zALE/q9IUIWAV9gWF5e5OWXn2U4nrG5sUOQpsXGwhSbBSMKGV5tzGN1YA+GCaNRhDaGvYnmduiwnyU4ucdf+uxp3nrtNpnlUbbKiEwz6M2YdyOSyMIaTTg1b3PhdBnjW/z2D0KqnmanO6GtFZcvLnN/e58L5yqszh3fol97oBmUC+5u7BFlMdJocq2JE8Pu7R0+LI/4YOsuVb/E/PICWze3sT2Jzg0YgTaSeJZjGRurL0mqOVM75rOffoG5Uy6WsVmpHO/pqo86IxGQmeJ7QWERKBDkpli/Rwf6o88R+FN4FTwOdLxNy7tEnBYn3rm5DnmWkqU5nbk5Ll9b5Uev9uj2B7i1Mj/4/g948fNfZ81UORzEbA32MMMu8e03uXp6kWazwzSMMBharTakKd1ulwsLpx97TJ9oIJ8EAbZf4eKVT2FPpzSXT3F30GVh+RTNRoMsyYnDCGG7DCcBGxt32dvdRSqFdFyUJbl4aY1LF89w//YCm3c+oru3idYZmgwhUqSxyP8Uxyn10DixaO3Pjm7mH7z6PdaWVpnqnFSDsj2aK3M4JTj71FUqWMxGY25++zVK7QZep8Nwa4rzp9gLLMy3EAiarSpZGLO+uc7G1gaW4xCHU+TMRaQaIXJUbqjPd3DbTRxlUfI8ymWHJM/JgTBUhUWaY+H7DspSSGlIsxQjjtd5dp2ccHqIbTm0FsrUag1sr0az2sDzXKZjg21ZeG4hwVkulUgiB9ezSZKIcDZFKkmvO6BWsUmikO3t4nPJ85RarYqQKZY6viW9PsmZ7PeYnA1Yml/i7NkGz+zv8tGbu0RpxuHBHpPBPt1JgEgcZOIQTWY0511alQr7wx6xgcwqHhznFtfopprUKNpGcGmuwytvvM77+8fvyIt2a8F0NObSfBsjc7rDCXc395nFAaPpFGlZeJ4DCDSG//Fv/1csLTWRIiFNc1555W1+9Z98i729PouLy+wfHGC0eXh9Y8yJqZVompJGKbZlk0cZvQO4cOM0t364yVsfxVy8cZXJYZ/xfp9zp+YIF5oMwyGtmuHO/UPeet/w9PUzPH3tLH/nV7/FMxfatGsVbDvH2B67fcP8IkyD41ManuNijGF1ZZXnnrpRyAbYNvd3cqbZMiLZIrBDpkHMT/sdkqee4dVUkTAFS1DvlNGAySEbCyZ7E5q1BpbnkaGpdSRl7VJJTkhFGk2OQRjQQhR1tCP9EonEHOVkBcWD0miwBA/v8+ME88dZveEwwDRDclHBtTSdZoU8FYyrLtFwxrB/iBKKWruBFgbbruB7HmtnFqm0bQ56O0g9ZePWexgjKFVqKL8M5DSbLZqlOvf3+tSqf0ZTK/1BwOnlJWxlI0xhZWW6Xe5vbpGkKVmmWV5exXUdol5Grz8kTjLKZY/xeMTdu3dY6Mxx5fJlOitrdBbmeeP7/4r9zbsYNBhZ7KpPMHF9FMoYMgNIheW5dDoddJLz0qdeolFrEPs2zbKLUFMmrsAsNaiVmrhJileCC1eukoYZdr0FB/voJH3sUN5ulNHaUHIVozAlzCKSPMZTLiXXJpuFiDTFLUEyjRkTcWaugVu2gBzXkthGEucaKW2EkEUAV2BMjmUX5hlJenyOXJYzpNTkeUycQpomzC00WV5aKZaFMWRpiqUKsbM8Sznc32Nj4xaTaR+EOdKNgPn2Iu12mySNGI17CJly89Yhvu9j4fH0s+c/dhwtt8zgsMu7u/dYPnuJaqPM5csrXIrOEMUZFVWirSoMREYiwasqJjjc2R4SSIPnuagswbNtzjQbjPUytw62sUslrjQbTHYOePPeFoMTHihSCEyqycYznFaZ/ixlY6/LYDIrgoNd6HEYk6MNvPy5T/PyS08xmUyYTEPqNY8rF9f46tdf4PvfepeJ1mRGIynMsoU5CkYnzI9m3WKalJnMEhKdcufmgJ/5wtOcSQKG/SGN585QVSlVAoya0VztEO1rKtUpfmWJ9b0Zm5t3+fBwkziHM23F6UtNurOI+4d7DCaGceSQqONPbKNZUDhQacNzT10njCOSRPDmzX0+ujPipQ7obIIhZ9yx+ObTn2NhucP6/j0Oo038hkuWWyR2RnCgCbKUKMmotdtkGq5fWMMA2d3jU28PcuSFb8rRLlsIcl0YgDxYd0cvF3UqJRBHv/eghnVcQH+cyJGmIzbWX6PRvkE22ububp+dzTt8+N5bzHdKXDvzDEEwYzCQtObanD27xsL8HIP1Poe7Q+q1EmibuWYdo0cIaQE2SZxS8msY5VJv1Km4f0ZlbF/53vss/dyX8MsKoy26+4fsbu0wnE0Z9EfcePoGlWoFYwyNRoOXXvoJxkemzAjD5uYmN29+RKvR5LXv/4C1lXkynaMUSCQCeZTxfvxAnuYC5ficOnuBxlwH5dh45QZ+uYwtLZTlEAhFab5NKDSzDOLtGXmeQppxbu4MwWiMqc0x9bdJ4v5jC5e5diG8o0QRdJtzLVzPplzx8F0HkwQ4toUQOUkSEoynHK7fpXl2FaviI7TAczxMliCVBlHsRH5MxBFYlgUcv1B7coB0XVzLxS4LUs+CcgWtiiLdbDZFSQlBThSGlHwXKWKyfMT9zXcJwymzWcT+3oCf+elfQEpBs1mj0agSRBPubdwsTCGy41Xl+ipntd5kq9fFiVKcsqHsSKazEcNRyNWf+GlSLdgcv4txMip1i1FuEYdlbFdSL0uyYcrVpRVWWw2mbkblbcW03+XMhdPcvLvO/f6YvHS8vLA2miSMqduFd+dhd8R+d0iW5Ud57mInaERRUP7LP/eTjMf9gqnk2bieQ5alxFFMfzrjcDRGCVmQfQ0nR/Aj1OqCJVki2UwYa4s7hzEffbjOzz1bQvX3mPaHtB1IXU2oNWEWs9Qs8cH391hcbbD2Yp3vvN5jez+lXlHcuCioM0Bpi8EI+qHh7Y+GaHl8LvbX/vk/ZG+nh84Mp08t0VroICzFnZ2M7c0J/VwT5QEt12OqBHu7AX5lkZ0fvIHbqULiYmtDOlFUdESnU2N/d5fvvPYqixWfTz/zHJN0zL44Xrzrga3joyJ5f/LrP17/Wjwow/7RRflxwfxxdu3bu99lGkLJ+4C3Xn2Pne3bdPfvoZTgp//r/xIlBbnJGI/H5EYTBAG+Y7O8UKc3GNFpeNSqDYa760gkUtroXKK1RKDwqk2M7TE3d7y37KP4RAP5rdtv8Cv/9y7Pf+bTfOrpT7F2/hxrZ8/x9gcfcv/+fXy/COKWUpTLZcqlEsvLS0gh0cbwzDPPEYcR0/GEX/iFX+TwYIeNdUU4mxBOJ1gCNAZ1UhkeDWi00XzlGz/P3MopYiNJpCTXOVaoC2caMkgMVqqZqELJrBUVR7pcWMhcICNo5lXSlYs0kLz9g+9hspDHebYbWQIDGkWeJ+hY89nnn2dzc5PDwRCtA2bGYFkWruvhCsX4cJtp7wCvXmdu7Qy1eQ9tDJVmB9vzMaaQAM7ynCSOSdOUmlc9dhw7wQSsPibL6QiHxCScqvpYjksUxJTKZYKw0IyXEmLj4CqLZucsL7ZOoVA0ak0cy2U2mTLXadFsNXEcxWF3n3OnzjEeD3Gd43fCSd3n1WGfKzrm++++xvnli9yodfgPL71EnmTc+lffIXHga6tXKDkeOovY9YZ4p2086dCoN7GNJJmF/PDd95lbWuTlM2e5NtfiguPzt3/v2xy0G2T+8Z+Nzov8b3c4IUwSvvPGByRZsaPOhcEYjUaQa8NP/eTn+Te+8hlAsbN7m73te/jlGkEQEM1m9AYjpATftVFCECcpqdZFHveEKeLg0KkI3LU2H350QJ6n/PYrO3z1pYsstDxuba0zaJQ4tdhETgNKNY+436e16BJGIXpb8/yK4Gv/eZn/7R9l/PavjbhxUbHSzFhCEFuwsZ8wm+0eO47/45/+MkZrPEdxNergbSuyJKOz9u/xM898laX99/nHr/5L/ErIj7a3Waj7tFyLr50+RZhk6AjCOGeje8BS3Wfr4IDuMGBbZLy28Q5mJ8d3lzl75uqx4zD8eFdeyL4WwVsdaX4/WqhMkwSEwFbOkdogTMZj5JFb0KOy1/JIrlhr8/D6xyEdLvJb//if06y8z+mLTV56agX/uYsIZVGRNltbm4ynQ1y7RqPV5LlPfYokmPL8jfN846sv4mqLJNP82z/7ddIwQFkO0nbZ39/l3Xdvk0qfIBYcpMdvfB7FJxrIL19aot6Y52DvkO9Ov8vT155ieXGJhcU5+v0ueZ6SJAna+rHLjW1ZGGkwpuB8j0ZjSr6PZdksnzrD4lKH1eVlfvTadwhG+48lYwtg0Bid8+JnXiRBsLV/gCcskklhl2YBCImxQOUaK4ZUGciKhZxJg0ISjGdkJqPerHL+0lX2tzbY37z9WGcCKQS286AwmXLjxtOEYcDBwQHD0Qi7UkYYTZ7nhFmGhaYqNdIY0umYnbu3cW2Laq2J73p4pRJa81Dmd2rA5BrrBCMFnUtyNLZv4zhlmo0OtXIVTE6aRwyGh/T6BwyGXdI0RlmSVrOGZ/tkqUFhE4Y51XKd7v4OYTjFdi2YaoyBe/fuM51OsO3j78pgMiMTNuNSibfW7zINUpY789yYW6C7uYeaq1ErOUSTlFNeifmlFTame3hK0PFLOFJxOByyPesRZDmVhQpnXIut7bu80ttjfLqB69eQyfFH+CRJcVybzd4A+1Chs5z5doOtKEY/oJkagRQZN56+xmwyxPPLvPP2B8zPN7l1e5s4SZnMEoSEaqnE9QunGQ2GBGHMZndQ2KWdND8QSJPRrpaoVW2k8pBGMJIWl6+dp/x6j92dCbNBzrPPLUEwJqWFX0vI0oiq6zDtp9x8dczPfH2N7716l7Lb4NpKhSQa4sq80A6PTvAwtTwaNYd62UX5XmEK7YGtYvJki995+9tsfvQal8+tMR6PaImY0tI848mIwWiKV62R5oZG2cF2LUZxzjTTfPrl65y55qCly9rcOb7w1GdPuCN/FA9phhyxgoQoaJ7GMDgyZvcXF4pTujEYUfSj/HFa4h+Vwj6ZuRJnezz97Bnm6h6ZdCBXZFqihGTYGxGkIVmaIXRMEATUWw3yLOP2zY+4c+s2Tb+Ocn2wFCXPQUoHy3HZ29vCdRXLS8vUgoxUP35m4ZM1ltAgc0O9UsEvldnvHuL6HkEwYTTuY1mS2WyG53nYts3e7i5CCBYXF7EshyzNiKIYx7LJMs1sPEGTsnTqMpenIa99+58+XpILMFpQKtV49sYFeqMxmIDBOGAQRMwsjZMZsKwi9ys1FSOJbUgsQyWXmFwSxykSgSMkL9y4zmKrzu7N99jbvPVYRzQpJbZtY4yhVquhNdi2xeLiIr1+n1hr8iNfUtt2SNIcS2g8y0YK8IzN5vp95pdSSpU6SRg/3FU4joOjLLCdwtT4GCjHLxxwpEKaElWvgW/7GJ1hSJFKYzsCz7MwxCRJwPZe/8h6a5nVU6dRwmU2DpF2zm/93q8z9/YcFy9e4Etf/CKWe5433nidg8ODY8cxC8G2S4x6OeJimzujHh/cucX5xVWC8YzTT12j5li4bYlKDOFszL1bd1jqtGh0JEmWYshxfIfvvfIq0rXZOjxgc9Ija3lE0sJFU3KOP7G99dq7CJFiKcl0NEFnGbpb8JaLyZOBVAjtMOqPyPKIg4MZg36fU2eWyHPN2299SBxlVDwPBdy5u0EUxtjKxlMWcZ5hTpoiJoE8xPMlcy2fxaUlyiXDH74Z8c73+/z8ZxQty+dgZ8DO/UMW2zALc/x2m3i0h2LG0oUadt9nMumzMtdh7+6IaD6g4igyDUEmCM3xqbefe/kFPE/iuDbYEksoMgmurLIkfP7nd39E4lnEaUwwm0FZ0u11CSYT4twwSvrEGhplnxzJOEyQlqRVqlEvLdAfpmjjMghSVo4ZR6YNObIItkcc8ocBWoijta8x0tAbjjE5LC7MkR/daKEshJBHqccf33ytiwCulDjRpQggiKbMLbbRUY6lfIQSCKlQlkTrqNjs5RJhF5x6Syh6h4csLS9hdGFMoyRkWcIsyIAYmLG+vs5kHBEmEsevkGcZ8HgUxE80kKehJvEi4mhKvdHEYOgN+kyGPfJkyuHBLo5foV5r4Dg5Uim6h4e0Wm2UtInCkDxNGI8nGGWxvrFOEkVcPHcJ2y0RZznWY5QapZQYI7DtEufPnmY5ial4it/Zvcv3gjuMP9ok82wWl0/TKFcLEXs/JXUEC7JEbWtIzVisrizR6bRpN1ucPXWaZrXMubUzvPIKJ5+bKTrIHsD3fZIkw3Ecrly5wubmFmEakCYFha0wrdboqk8p05SEoubVccsuWxv3WVo+DaY47httcJTCs2yEPnnnl2QCZXsIHBxZhdyGFIxlsJRNqVTGcSxarcbR5JsynHUhh1LFJdUR02hCf9CHPKDZKbG+8SHXn77E7uE27VaHU2dOc+H82WPHYQubmrBouXU+3Bky7u6z4CjqSc7d/QM+mEUEkylBPOCrzz/P+fkVLN9HOA6xcnjn9ibffeMt8pLDU089zYXrzzK5d4tezyOzBZt3blGu1k6soeRJgpQZGEm9XiGOUpIke2gU8mDnl2P48P0PEOZrrKyu8KUvfI5/9hvfYn55gZWVFut3drHyolCcWwotbcIkgwdMihMe9qdXOqTplDQOOdXyCMcDrly/SKQ1H33QpfOTDSq1OpWWS29ng6hRoVpRmOZZlIHoYI9a9Qy+NNz54IBabRG5nLJwuk5/I0CIEKREyOPvx8LZMsIY4jTDcySWLSnJOmdnK6gM1vc2WHjuWcbhmPFshtFlbt29R6NcJlOKg+kUxyvhKkEcGqZJSqYU8TDEVQ2S8Q7vf/gRvcGAp5778sd/Lg+omwj++NbEPPKvxtAfjggnIdeunisa/YQgihOUVLi288d25D/++jin+bnGGaJJSK5zkOJh45+QHDG1siI9d/TgF8DGxn3On7tAnCRYlo3vefSGA8rlEtZR+seyFGmS8Pbbb9BotmksXQAunzge+IQDeRRNUKHADR1mwQQRSaIopL+3yXuvf5fDg32efeFzjEcBruMyN99meeUUuTZkUUY4m+IoRbne5Pf+4He5efcWV85eZvPuPQb9PTJtF918JyjIWJZFvd7i6pUb3Ly9QbvdpFqtc92+wPdGh3RnU6qZwjR9TMUnnKR8MOzSKTW40mrRmClWGzW+/PmXSbOMpfllfLtElsT41fKPSawnwLEUxmgc28axJEJL0ixlab7Nc89c57XvfI8s00glSHJJEMbMYoNlYqoVTV5ucqbRwnVrzKYRNeXhOC5aZ2B46IFqTvAzdf1KwabApV6Zo1qqYwsHJVxyI3DsCo7jk2YRWRajlIuxJUkUMZyM2dnZIgxmRFFAMB5iKcnahVMMxl0m7425cP4KrU6bZuX43YVrDJ2SRCnNa+tdouGIJd9mK9liGiaULcMgzEGX+UZjmbLtcOXSJWSWMU5Tbk1itvISYWq4XrNJwwnzXolgYYF3e9uoaoVcSrI4OHYcmdbY6sjpJctxHBvPc5ASoihhEiZIKblxocNTcw6He3ssLl9ka/0Ov/mbrzGLYv7SX3wZ3/fgwXE+z4/qnIV7lHmEFvdxCIMxrqOYW5jDVzPu3BlzeH+f82cyLj2rUO48Se6iahZWr088ENRaJXp7Q5RtIcsL7O+MiOKQJM6JBn38ikXmu6QioFP3CMMI3zp+fnznzgYShZVrcIvOab2T49+6yXtb65SW2/hzLfbf3uBgPGFv5DKMEuYbVQbTmPEspC4k/aiQlNDGkCYpumKYljaJqlNm04SKOz12HPpxXHOEeGjQHowD+OrLD1diHMdF0V1U/rXrFAXTojf1pL9R9+dJxnvkIkIIXcgDCIFtSxCG7MiQXQhBnucYU5zmgjAoLOcsC2VZtNvtwqtTFxQNpQRJGpFpmAUTWid0ZD+KTzSQ+yWHhcUFRtMZ927fxLJsPL+E6/o05leZhZpud8TtW2+xtnaaIJzSqNfoa43juISHPRrNBlE9wU0la+1VjOVQaVV5+Qufx6Rjvvutf8nBCTxhIy1OryzxF774Ivu7e9y8eZfeZEo0G/ITMVyyTxN4YI0svHGOFhU+7V7GDyxqPc00cLg/nvIP/s9fJ89B5TlBHiAcQSXOiJ2i88w9wfTY9xySJCGYjtFpjKCoAziW4Nkb11jqzPP+Bx9y594GwWSGW65hSUUUhUxHQ/bfGfPmu29x6fwap8+eI0tTdJ5RrlYe+qH6vn9kS/XxKEeCmlyk6azwdPMz+H6NSRwS5QZbOeTaIAUILZC6sEGT4RTimGA4RBvDfKuNoYVudyj7PqPRmFFvSK1aI54FZH75xCLj4WFKtz8CFZJ6HqYyTzfLKY0llvDJNw5I4pjRLOLu3/9HfP36eZ6/9Axb+1u8s7PHK7cPGQca7SfceHGJ+IPX+eL1z3Lv/oQoTklIqZY8cv/44m8SpkgsLNdiMBgX0igCjDYkWcrv//4/pFpy2NhYJwgDap0OSTim3WqyutIiSXMG3RFpmhCnKRpzZPNVmDcbXXStnHQykKZEnCk+3Jgh7BI9nXHz2+t87vlFPr1W4/Zeg14v5/Z77/Cps0ULeKlcpX3Kp3/nJqP7Y6apzWhcPKxnImPl6iLOYp3wTsTBoEepWj4SDvh4NFs5aRKRTnM82yYzAnlas2d6tNYsPvPsaYQY8dbU4/athH/x3n0aVZ9+Y0bVgK0EsyjEEZKFThVlWWzogFnQ5w//YISjcprtOif28T0SyMUjc/pBgfJBOjNNM77+ta8RTEJ0rnmwqRJSIuXHG7UX1znZO3TzzuvcurdBqVLDthS+76OUwnMt9vc28DyXztwCWVYwV+r1Omtra7z99ttMJhO+9KUv4VdKCCEol8soZWFIKZUdHFcx6veYTQe0O39GWStZlmLbDpWKpLu/R5ak2LaLWy+xdvEat25t4HslyuUyYRgQxxFRZBNGIZbjoKczKtUqIgr51Oc/S8lYxLZCWYpKySMLfcIoJ46Pn5iO71Ou1UiykKocMX/xFFPtYCUhOswYDUbMwoipMZg0J88zxllCkmpSxxDbNiIXlLWDEA4iSxHaQuc5wWxEfP4cIgio5MdPiCAIHnqSatvGUkWR94GY18JCByGvUms02NzdpdsfEM0CPM/B9138UonJsMd4PGBurkW9Xmc0HpOmKZZlkaYpjlM0rRwHE2hyEREGQ3o727jBhOpci1rLIjOmaHwxGalImc0CoijEVi6Z0LSb80U+3rGPioAZJs8BiW05uK6PUhY6L4qwx6FUqzKLDKkWuEKghQEJaVYwPYJpiuuV8TzFJOnx7v4uC6ee4dW9Ibf3D9HkxGGAV/XZD6dFEUpIwjjj3t4uq+eW6LSaDEbH09xc3yJLEibj5CgLUgSDKEn50pdeJp4NIPOoVEqsrKyy0F4gT2OSOOVTN87RbM/x3jvv4XgumdYY+eCQ9v+y96Yxlmbnfd/vLO92l7r31tbVVdXr9OychTspkhK1hFogWUpiy0Jiw0mEWEYUJEGMBPmS5EMC54sNyTEQGLGcGBIsOCAjkVYkWbRIUSIlkcNZON093dP7Vr3Ucqvu+q5nyYf3Vs3I1lRNEGAgAf18HNy+9cx9z/uc5zznv7zrOA9HPhfrDHEo6CaeihGdruf+juK3/niD714I+VK1Oc0AACAASURBVNabI3CaxXbF2VOWzpzk/GtbLJ4dcHJtHj+pmPQtSaeBchJTOrypyLzBSIGUmizLEByuTbR5f1xvPHnNnnXO4SpLajKSTsTGZkroA5ZPLrB+dpGNtzfprbd58See4dHVPUaXHxGWjn5uCNICLwQnnpjn5Jkujpx7V8ZMJiCby4fmcVTUY5fZXYaQB+vNOXfQGR/W1BxouRxxgi2rPq+//m2acz2yaX5gnBwGCoGl3W5y8uQZTp1+hmbzDMPhkLW1NV588UWUUqyvr5G0WxRlwV5/F5Uo4jih0YgJdI2Ht6YiHQ/e9//7B1rI2+0WVVliLDRbHaQURDMT1IXlNR482GU8GXH69EmMrR3gyzzDG0vlc1xRUDpLmGZM5hSRkWgkXkBZGm7fuMM3v/ltOu340Dyc9yQLC5x45lmuvPo10hPb7LSXmWvELDRClufn6SE5XgpEaamqisxavIGpdWRZSZ4VjKYpt2/dJ02HFD5FlY4yTBi9/DJBf4fpdv/QPPa7CmMMWgcoKVFKHiB28iKl1Uo499RZ1s+cor834PaNG5RlSafbodlo8nDjDs1GhPOWMApoNhPywuC9pyxLtNazYv7eUaUOqw1pNeTmtbfpHT9O1Eywc4agGeBdiXMVZZkTBAqtGyirmO/O198fhAd/ZzTokxcFve4irVYbITRah0ilZ0fX947cWConmE4zwjgGDzoKKY0ht45JVhJZSVMLVNBlXCWErQDXarHrPEJVyARaS022TI6xmvN3ryNCWF9dYZxNqe6nhNHhy77T6bC71a/nx64+3jrneOa5M/y3/93PY/Mh4+mI5aVV2u0OzntUoJnrzPPxDz/NMM1ZWl3hwW6KnR3VZ3vSQQ/u/ftArQSGrJjQbTZxYkpqA544u0RuDe1I8uaVLYal5dhyg0Z3yuefjNjMKvI7BRsbFcuLq+gk59aNHY4vBDRjQWdujkZnlXRyBxeETCZjkuDw9ZEkKV44MlOC1GghsJR05g0qzAlUBoUi0DFz85I4jnj5c8usLBuW5hsMzq2weXNMdi1nb1hyb2/E933sGdpxi9G04vTTy4zHFYvzRxGC9q8WxExaoz7xOmGxoiYCSl8fn8JAg3B4qaicJbeO3Hmkt1SyAnQtsnVghDxjaPma23FYjAYVK8vHycqKbncRZy07/R2m0xyEIC1LKiRJY5FuZwFQnD0bc/fuXabTKaPRiLUnn+bkWoeWWmIwSXEECOlpyYpqUhEmIa46nJH97vhACznCUxQl/eGUXm8eqOWqbFXivOT06dNsbj6i2601V/LcIn1zHxNEUZUU1hBv9jGxIrUe6SXChSA1Fy9c4vLl68z3moemoYXgrXu32fnKb/C5J08znutw5cwKd4aPeDaCE7akGSfESYhynrKwFFVJXEBeVQRZxsblyzzc2sY7RavdpBUnaG3ZUyHi5DIugLIsDs9DKoyUhLNuHARhGGOMqam7jYS8KDF5SRIGUFU0koRzZ5/g9OnTCAHdVkJ/6wHpZEy50MVQu9DnZU4URVS2QvrDF6b3GqE03niGk138MKI3ndAoMlCeWIO1JTqQaF27oDdViyiK0FoTBAGB1uggwJYl3gu0Dmg02iRxA2vr464/ggiTFhVlKcjyAp2VaAkhHisDjABvBdU4Y9rwxHEtqNYyezzTa/GgM8+N0QatboelpR7NVoAuYHO6TXehh9vKkA1NVVny4eGz2CgOsd4jKksQStK8wkvBX/vrPw4uo9WaYzIZ1FK/M3t5YyqOnziBjhWtvGRawd0HuzjvEDMZZseskIt9IvnhG1sQe0QFwlc8e2qeuR1HW1RUFhqhY+HFdV6/ssF4XHL3fkb8qTU+dKyNUm3eevMh26Mxp9ci1k+FPLhfIXyMvbLL/OlFlo6HlN7iREBuDp/FZlkEwmGnEVFPY51BihDnLMJIvArr0ZuKmEwsqqlZPrmINRovPK2lkNZSj1Z3wu53JzwqpiytaorpmNFOQXexy6mTSxzrHl7IeRcz03mB9rVEhKOWMRCBxBmHs9Af7bKz1+dsscYb3/kOvYVFlpaW0dajfK1V7a3FaRBCzp6FAF9DSw+L9twS3e4ubjigkXRwzpGmOclCm05vgbgh6t9DSNpzbeI45vLlS3z5y1/m/v37vPDCC/yVn/n32H5qlY9/9Ps53mkwzQxxMEcUthD0cc7PGMTvLz7QQt7v77DenGewN0AFMfc2NnjxuWcxRcF4OOTkyXX6/U1Goz0m6R6R0vimx1cVUagpy5LKWbav3qK31CbfZ3SaEisCbt+6zdZ2n+n0iKMzip0y50+/8XVOdn+GMy+9TLyyzL2yT1jBfNyiijRxGBNJRZEX5MpSGcFr967QDEu27l4hruCjH/0kSoRktiQd9jGTHLm5g9jdw47Hh+YhUXjr8Ra8F6RZjg5CjPUY66mKCePRhDQtyNKcK5evsLiyzNJCj267iZCSkyfWoJyyt7NFa6FN4RzeCLRSlK5ESPEObO49ovSezFYoLGU2xIwi5vpbqDikt9BDNyOEBCUk3tUEpTiIa6lcIWuijPUo6ZEqwFiPx81my7KWGBWSsswPzaOqKsqKmnZtbU1qcgavJF7IGsNdOSaiIIoFzo95461dpk4jnaPRWyGfTpimY0wWIGzIIB8yMQWj6YSRgVajeaB58l6hlax/N2eJg4jVE3O8+NKzLC92a7hhY7ovKU5RZEgdkucjnDOESQOnQpaXFgnCsO7wZnXh3/yrR3XkjUASJg3mdMRSA5IlQViljLOAuNFir9/np1/u8eRyk07DcGJBM9hJWVhcIEoKmqEizXZZ7CluPQzI8gbTLcGp3ZxxOM90eAmcRMjDC3mrYUEYrI1Iuv5Az8hbjdYSJQW6DPCFYq4T4IuIuShAKoGXoLxCi4De0zHnr0o+fuZpzp5cZudhwanjCVVmGW2mhOJw8S4pmAmv1fcMHocpMwLpaOmIvHIUxpKVhis3bmKMYav/iIcbN8mnYyIU80vHGOxWNGPBYLhH3I5od9p457E4vOBIWGij2aK3sMQwzYiiiDzPCcOAF158kaef/hBBJOnvbNNtt5iba9HpdPm1X/tVLr51kbn2HK+88goLrQZR8yf59Su/xksnBSef/ASBXkQnSxh3g6pyNNuH3+W8Oz7QQv5g4y5RmDDfjsmmGW9dvMTTT50l0h47HiMDxfG149y/d5csHVHpCG8c41cuEwaaxQ89Q5nl6Fab/NEm2xZ0I2Gxs4AVFXce3OPRYEB4eB0H61BSMP/UWfJmQHd3xCdVxJXePLujjMWzZ2h6h/IKVTjy0NIsc6bFBJm0iIucRjtBTQouXb/E9t1NhNQsLLYpVEDv7YfYwR7N/PDCNR5PKYpyVsAekeYpw2EfIcUMMz/C2looyHjDytoxGi3N7t59kkZ9eeOEpb3QY+v+Q+TGAxqdOeZaHaIwot/v02gkqCOgkMN0m73hFt5IAjFHKQ3RhmacjlkeHef5Z86hZN1VKhnUWuXe1RuQMYzGI3QQEIYhubGUztOMQ4z3FGWBrSzeOfL88BNKVVUIasGvqqrwzhCFAUprHAJUPecVeYLdrVDrkn956Tpzi/PIpIMxDmsr4iTAWYeQIVMhCUNNc36Bew/u4J0gOGIGOhyMUEpRVYa8qPiJv/IjfOpTz7Oz+RBbtem2I1aOr1EZh1aQZmPKYspkMkGpELSm0WhRVeY9EIazjvCIgpEEEDcSpAvZHKdY4eken2N0Fy7dGvLxF5r88POLtIuC3Z0BxdYmsUkpRw84tdphcX2VMBGY8YAfPB0ggoC3Lu/yK796lbc3CzqtEBFqmtHhaKJQddBKIBsK5y3SCqJAIiSkWUojbpEkCUEj4ty5guvZI3pJDxnIWuaCuuONugEXdZ8XnjhJm4TL9+7hZMFnPnOOazcLgiNuO8vKYW2NEpFSYjCMR0OGOw9oNJvEnS6DsqAVN2mGMeNijG0mnPz4R7l3bYM/unCZ008Yup02jQYQBHSNRRdlPRsXEucF6gg45qOtHaK4gdQBSZKQpilCCJYWF5lf6DHX6dKZ67HXv8/eYBcdKK5cucJ8b54kSaiqij/99qs89cnvY/e7X2H9hYSTp58nTlYQYYuVtVXm5ru0u53DF8i74oNVPxwNeeXb32I8Sclp89Qzz7K1tUk116KpUi5cuMD3Xnud3/jSF7FVxfNPnuWl73uZ9nJEG40pdhlsKlpBgjIJYmzw4wJhanH7i5cukuZTwsbh1Na0TEnv3sGsLnD/mSe5s9pjwTj+zqmXeCnoMjecsqxD2kGI6khy5fm22eOPR494drnF8BuvkQ5HJF7zVj5gXG2y0myT3esz3twhUIYAjszjwaMrGGvri0lj0IEmL+u3O0szNvd2UEpjrScKGyTdmEhLsjzl5q3rKKVpLszTXlmknabcvXObc888Q7PZZDwa0+31yNKUwB4+AxXNEo2iLCqyfIdisseDt6+SNOc5dfpJsnKPlaVVet0ujThhPJ0QSjejNwuEkERSILymdJak0SCKY4qiYDwYooTkzu3bVKUFfvo983Amw1iD8ZokCvBZRba1S3O+iws0utukiitUaXHeUnrHfLvDU8dX2JkWPCwmyMTiAse0VBSmQriccrSHUHBm5Tj96ZDiiFVv84KqyHHeUaWWf/Fr/5Lt7T7tRsjvf/VP+cF/51P8wi88RSNpUJUF48mAaTrlravXOb22ymA04ZXXzmO8qUcrCPb5KrWs6ux3PwJJ9/xTJ9jYGnH3wYA41Kwf05xabaFMwXAv5wsfmyfb2ePmwwpdFdh8QGUl5YMJFs3ttx6RGwjbEROrqIzn1s6Eb98tGFURx5oBGRETc/gG+5HjJ1FSI5EUvmJcjknzKQhPO24iKHHWIGXKj3/qGZJP/gDXsm/Tihr0dyuskZxc7RKIiAeDXbgAxyeL7OwWnOmtkt9r0SVmoTrc8OMP37x5gMbygNQS9rb4zq//U9q33uKkOs7Sk0+y8tEXOTbXZLEy7Pz61xi8fRdxb4OWr7hm/m+ErrheDvnEz/7HLH/0IxSuP7vUlvVoRUq+75kn3zOP2/fuM8kzdvp9qrTg7t07eA+/99Xf4sady/zID/8kWukZphyKIsU5y8mTJ3n++ee5ffs2F996i1/5J7/MMgFlscTd4Pe5NjQ0haCoBG+9fYm3r+T8p//hzx2+SGbxgRbyfdeaoiy4fP0Oly5f4uTpE5xYXeWp02eZWMt3vv0K/Z0+1hjut2PmrzYIuk0W4jmaVUYryyhaHWIp6PkAApiMh0ghkMLRboR8+pMfOTSPfV1oM5jwh6+/xutbd1i6eYulhQUu9rrc2h7S7szTixLmhOCpRovPPXEO0zjO1XSHbz3aozhznDQIyQcDOn4V+3CLna0+Ns2I2/XYITiCGt/f20RrjdaaPC+QUmOdxRo7u0G3BDpGK0EYBvV3BhE6gP3jZZwkBGFAb36BveGIrYebNJMWIGi3mqhmA+EOh0HmzhJHIcw0QHAOby2TapNL1/bAR0RhAx2EZHnOeDSg105w1hLFce2kEwaMp1Mm0wkSUUP1igItFcPxhPFkjI4OZ1RK65BSIFHY0iC8x+EY7e0SJk1sEOBt3ZWNreBP396gEXm2Lt0gNwIfJywsdNBRQCwlzpUU4wwaEVVQy6ge7y0zyaaH5rF6bKn+u5MU8DjrOP+9t3nxxadx1rG906coK6TUFPmESTrl8vmrRHHAaDQmy0vu3N2k3x+jlKox5AdV/P3LHN+6vYlH04oUYRjgvaM0lmY3oDUfcfm25+blEdPdipeeX6XTMFy7NGZuPmBlvYkUhs07Y3wVcmN3wtbQkjqJDmKe7HSYFmP2dgqMPGJ9TBLyLCOKNNMqJQoiGsRUqUFrzWAwJvABg0nKuNhmeU3yJ+f3WFsvkQSUxjDOM+ZbHVbPLHFufZGd4YCwATvTCWfMEtfvbLI1NofmMUhtDQZQAqkUzjgaQYPuqZNcu/nHiCpi8/417mUD4igmDEM6C4vs7N1mvHMT48aMItizFaPVFYrVLnuVxRqHxxMIiZ+twcMiDCWxkywtzDHa3aXRiHHOkk3HXH7rPJ/4+GdYXFyqx4vO4ZwlCBWLi/PoQPLEubPcunWNrZ1HnH7xc/zh5es0Tm/hUOyMU+5tDQliRaT+guLI95mM+zf2URQwGg35nTcvcPH4cWQUMBmnNBsJoVY44Sj3hkgc09hhCkMxnpBHMc3JiLEKIVZEOkF4OHPiOMd6n+Ozn/rYoXl4JdBC0vKa/njKaDdgc2BoDfe40E3YXT9JGDqCqiQqC1Z2d3n92i1KoXg4nTL2AXJhnao/Irnex2/cZTzcwTiHSiLkvpDPES9tI5kjCGrYXpHbmQi/xFmLUppESzrNNlVliYIEIRUiUGhVY2GtsURaE3gHccJib5nt7S2Gu33mOnM4U9JKGvBv8eD+bDgRMMkKwiDCltXMKs/XHl/CcefeLU6sn6PdLsnSCWUxxZRTqqqk2WwRaE1hqtqdxXmUkEwnE4QQJGHEYLBHEARMzOGXjD6zWFHVSANKtAbVTKiyjKIsEJY6L1FvcmXlcU7hvcY5RxI51o8dI4wK5mJJVpR4Acl8l2ksGd7bYw5Lmh5xhxIFzHfbZFmBsRYpYGdzl4sXrzFNUwbDKf3BkGPzXbIiRfqAbGeThafO8HC7z2A0YbiXcvv6Bt6+a4zzb4vwHRp3702JGxFBqAhDSxDIGSx1jR9aOcv1G3fJW0ssr/XwxwJkT7HmPIWbUiUB9+5u0C9hPEzJFUTtmLlmTCvxCKM5f70gTSP8EQ5Bc2mDFhE2tXRUhCwleEUQ1kilvDuhyEtyWbI3nJANp2zd9Vz47g2SJMFYh1CWtbV5FlsNoiCkEWqyZoouplRxwNMfOsPu5uHiXdYabOVQWqN1PVrMUbD2FNOzn+Ju0EIqjS8MvjC4vGIuCDGdFsGLH8I5Q+YslRCcffElwsYCwlq0qLtwJQTWO3Rw+EN67tlz3L59iyxVSFfVY0BfG5EURcHXv/41nnnmWRbn57DGUhQlcRxRVjnf/e53+MhHPkLSSAgGEUpklLYijELaUZOBUSThHgtzCa3kcPTdu+MDLeT7SmNxFJHnKVFcIyCqyjKYjImrGv0Q6JBQ1/jwIisIowipE6StoMjIq4KtnR02dnaRoeLDzz5HHEV8+IVnCbUiiQ+/7VVBUPtThgmTtERNHeF8RNyYo9leYvfYKrFLmLOaY0aymBoeDHdIs5Lh0DPYNdDPEY+m+J09/HiE8xYdaCKt0EK9L3W7JGkA4gCG6WbmvEEQIKUkLzPiKEIKSxiECKFA1Yus3gBqtlqtn6jpdOYYDAaMxxPCMGIaZHi3L2X73mGdIM8rrBEoD5WtC7JxFVEU1EdYYdna2cSYCoFhPJqiFBTVBB0ElDZFygApBYGQZOOMVtJCVAahPf3dLS7dOn9oHi5P8dLNMGEanEIHtSOR925222WhKsFanFXYMMZ5kNIw10lotQQqUUxGEwbbA5IoIcsyBtOcJamIpKLdOnzkpaRCCYVWEmM9YPC24tG9hwigqBx7oxF5nrO19QiZFZxdmmdzc4upAZBsPdpjMsp4d3Mn9oERvONcc1iMc4MII6SSlIUjnVqETcnTPQhLzpxqs7bcJNQxUjke7Tzi3sOUR49GxE3NZArtUHN2MSZTlv6gQEeOKAkpKzh7aoHRaI80P7zzy/OCMFTY0mJsibOeMIpw2lCVJVKCUgFxJEEWbG8P+alPvsCFax1GacpgnLE3Srl/e0zRmLD1aMzqfJuLOw+RiaA/znn53PPE80eIu1V1kyEF2JkCovWe+bUzfP6n/gZa1/aGpiwp8todqzffw8xOuDVGTiKVItAaaSRO2pn8s8Q4g1IKewSK5/lnn2F5YZ7NrS3G4zFFUWCMYTgYMB6NuHv7Fnfv3qHdiFleXmZ9ff0An14jXNKDd/z+/fusrKxQlhVlOcLjObm2SLMRsLbyF5QQVNNYA1qtJsePLZGXJbv9Ps1WkziJaUYaqRWBEgRaEeiAXApkVaKz+rLHOUMQh5x/6woXbtxBe8+JzhxPPHG6hsYJsEfM/HygsWGICBSRU4hCYpwkExorFb5UUHma1rKWaU6PY3SxwjTNGA4Vj7Yn9Ps54+Go7kJDhfK1SFW0jxY+UhGJg25cCIEO6q5SKXVwcsnLrFbJ25faFLMj5Uy+U+tgRpyS9Bo9kiSh1+uRlflMSyacuagfjkfdd72XUlKkOVDrbQshMcaSFyOuXD9PmVUIIYjDkKXOMlJZJlNQCqbTMc1mhyQKQQcoKYh0UJ8eRMmVGxfZ7N85/AcxGR6LEwapAgQaZ2UtkCQVKg7xRkBZgnEgHc5XhHHI4mqHtbM94jmBjkO8a3Lq7ApVVTL1OS0jWFlsUtmS4IhREwKMsQQyIMe8Y1jga7xxVVQ4Y6iMpd8fM09GEoXk93e4t7PHo92U6XCCmplyiJnE6jvFe4ZkOYJBGLcjjK+wTtVLynmEj7lxdZvc91leCdnbyZmMK4TUGFMyFyuac3PouCJoCnqthDg0BCJiWlY4BJNpyWSa0+sEPH+6iR8f0XHI2eajBAhFEEi0qte5tQZQRIFmN01xVcZ8LAl9xmeeWyXLC7IS+qMpRkjuP9rhe1c3MOOUhdYCq2td7ub3+aM3X2Ft+XCrN2vtTJ9fzEhas98paeCjCKU0VVmiI4eOK6RUBFGMDup7ihoq6gjCmiznhMRWhgOlFl+PZ4+KZhwhlhZYmO/WJ46q1lfZ2tzkzt27lBevkBUl4/GYvb097ty5QxzHWGtZX1+vJY7z/MBjNAhCwjDEGENlMnrdhEYzwpoj4Jjvig94tKKJY0kUhXzqY89inauTr2ztjC0FSmuUVLVwv/eUVYnzjkmRkVYFUkrCKKS3uMCLcZs//pPvcOPeA46fWCEwhspUNI64ZBSLyxRUZK2IoLOIjROMFxR7U8rU0tyBwAZUPmRYhNwfCeKhpUgnTCd7lHt97HQXacd0A/DtBtBAzogf+7rJSh/l8CEOuoUoivG+lpx1s917OBocUIyFrDHc+5Dw2uQAdKQJAk17ro3wkl5lWIkbdLvdmVxnvXAPizAMaxVG5w+gVGVlCEKFxzDJ73H91kPK3OCd5PjyOse6J/GuYrC3R1UVhOGE5WVJvDBPEoUsLi4RKAXK8Ztf+yqPdh9QysOZak99/ymMblOiWJirOyWTBUzvZdy6uoXxbRyOEycW8S5nOBrwMz/8Q9hY0c938aKE3FJkHqk026M+mclAWBIpmY52iYTAl4d3XPc2HuJc3fHts1Dq5tnPBK8cSgecv3yNmzfv8hMfOs13X73M7377AqWV5NbQDCWlVuTGHLjZ7O/t78cdCOCFF58AHFVpsKZER55MRiw8fYzxOCNeOUGrMSCYpGAswlmOrwSgIgorGE5y9nLJaAyT0tDrrSJNQQwMvePSxgCtDMvNwwtGs5GgA02aFSgRI4THGktZGkbjMdlkWmv0O4v3gt3hmChQ9fvrHKHWJBq8s/zU5z7MFz79IptbW3Tm2jz37JNc27nJdx9e5s7mzUPzcM4xTVO01jhXHdwvCSkxtsb8Gwda1PIKIEhLg1a6Jt95AEmeG8IgpCwrJK6WyvYOITRCBhw1/zLFBOUMzhjKaYW1hiAMObm2wlPnzjDX7XHl+k1GkxRTVVTGMNjb4/LlyywvL5NnGZPpFGstW1tbbG9ucfH8eYJQEISS/+o/+QWE1Njq/c/hPvAZuVLqoLgIIWYGTWJGn1UzFMQ7zh/7x5aqqmoqu3OYsmI8SklTQ6DrUU2W5zgVzDrbI9TtEOTOkwqwIYhYkbQifBxhlMbZlLwEVSo2C0VeChZLQZkOyYsBBUO8HqPjHB94vJTUY2WPd642wvg3NI//vBiPpjP5TFUfC53ECbC2LuTeCKrS4lGU1uM1aC8RCKJQURf4WuTJKA/eYrVHKI/WgqKsDSaOuuzcz3M6TWsFtqoCJ7CmdsNptwOEgyKvZ/dSSibpw5kTiwVhGQ6GnDv7HAudReYa9emkMgVpOuXh9gaZm0JwOJ5dLDYpC4dH41y9WWndJIgEoGhLz4c/9BQn2pKymJBXx1jqznN1e5NBlpM0JTrUKOqLTZtZwmaIUB5TFOxOMrT3BEd0wmlWomYvvp+RR94dk8kE5zzjaU5ZVDhbsbEzwlpFkmhsZmv4o/izGtn1Sp/pfgDqCHW3n/vP/xsQFoFFaoVxHmc8QgZIoVGhx5QGZ8DbumsXskQEGilnAl0OCuMxLiAUEAiDcyXGN5jkFVpaInHEKFKFOA9CqnrkZh1lUVEUBVXlyIoC5x3zvV5tot7r0d/ZQWmNdZ5Q16bgHoGXiqWFOYJAk5eOynrOdk8TN0O67euHrw9RG4w75+sRiLX1KFIpFA5MSSAlesZ5AGpGtpRYa2eNVS056xB4Zv9dUs/xsQj07JTx3tGMQ7wPGQ6HWOeoqpqZbUzFZFzx/LNPs7JyjHv3H/Dw4SP2dgc4W5GmKdvb2zjqxsBYi5QQac3iQo9eb47nnn8K6zwYR9g4HFf/7vjARyv1mEAhZ1R0OROykVKglH6X8179+f1Cbm1Nla8x1yWPHt7g4dYeK8sLtNpt8jwH7ZBKYo6YcbWkpIek6RQTI5HGo2z9aEWkCKMQhSIpIDGSpJRU85YyDymmMSZr05w61ETgw2VsnoGtNxhvLGomfhUc4VS0L7YTaH0wMqk3uppenyQJjaSB0CEqiFFBQBQEB98thMC4CuMNRVkSBAE6VASBJgg1aTatC7k5fGFKJTCmxHlTsxWtJVABoGs9ZaXIi7TubISk39+iqlLwnjCIEATE4RxREtJpdwiloyorjDWMhgOm+RQfWGx5eB7bewMMEUp5EhHgANVwoD2OitgOmXNDvvDpz7J8bIGyUrx2cYPt/hZVAg0RkucZzaRBPk3RxtJSCTqQV5AYoQAAIABJREFUTCuPSxQ7/T7eHb4+TGUQgUIKBbPRlp+ZFwC17rt3DHaHlOMpRVnVHqkC5loJ46zAWokUkrpt2I/ajNDNtFeOEkx9a2OPbqeH1hprSprxHOlkSqAUcRSSDrc4ffo0d+9soHXI6uo6b1+9ipISj6XTnWMyzmjNLbMzDGjEbeYaMUU1IDcCmSR44ajE4aPILK8OdEoajaQu4GVVy0Q0m5TGIEXdiHklaIQJrtel2WwSRRFpnmNMjYxqz7UQSOZabRaCCF95ZKg5EZ9ELB3+vlSVQQk5M2GuGZgB9anJGYMOI5y1tdJgUdRKiN4Ti/odw3uMqfDeEIUhVVXirandhajvYfIir++IDonlhTkEmmw8pkBh3exSnHp0Y7OSRiR57slTPHFilUePtnnjwnmGocI6QdRo4T30d7aIpOPU+hofevYZup0urXaLytc+BMX4/Y9WxPvR330cj+NxPI7H8Rc33j+Z/3E8jsfxOB7HX8h4XMgfx+N4HI/jL3k8LuSP43E8jsfxlzweF/LH8Tgex+P4Sx4fKGrli7/7Bx7nkN6yvNCrCShFzcDSStOaayGFp9NsoDwEKkAEgBCYqsIDWmm0jma36LWbTmUsHo9WIdbWqs/HVhbeE2N2Zw+Pd3j2hZ88akarV0KgVM3nUTUHBzmDD9caw/V3zAy7cd7inQIFr59/hf/nt7/I3/0v/2c8tfTq8Y56zzz+9s9/1puqQEvHfLdJHIS1WpySKOFphAFJEhE0NF7VprNVrhkNS8bj6czkFbKsZDKuUCoAPM7Zg99H6Vrz5R/8s6++Zx5/8gff8FWRMZ1MiKKY9RMn0VGItRVVkTMaDMjzlCLPKPOMssiZjsdUZYExFcbWWFprDWHSYnFpjSCMiKIQZy2j4QCkwnvB3/w7v/ieefzc3/sP/A9/+FMsNGMubJ3ne1cvs9vfZXVunuX2Cg82N9gNCm7vbNK0ESvzXdbPrnDj/jWsdxgjiIIG1sCAHZY3ToIQ5MeGlDJFJQqb1+SQ7/4vb79nHv/o7/0P3nuPkoJ0PMBbR5ZNWD5+AhFGNZbfe+SMbFp5hxeuxvx7j3DuwJZsHzLrncM6BzO3Gmst3gr++1/6x++Zxyc+9Un/yY9/ggcb93nmqXO88sp32Lh/j5deeoalxYDCCoxQDLfHrK+e5PTZs7z62vf4sR/7cXb6fbrdLsZahuMxc50W1hq0kPQHA86fP89Hn32J8xcusbh8jH/wS3//PfO4evWmz/MhF968yHfePM/bb13j+NIxfvE/+9scO76IMeZAzhUgyzKCoNYGEsJjjEEIQZqmpGleyyDHtZb7PhFmH3L83HPPvWcev/gb972vHHhB9WfE7cWB4YSY2erV4RFevWMFh6ullmfQUonG4amsxQtqyKuv3Wt/9W+dec88/qP/8cveeIvxDuPVgZG2x2OEIKSirWo53UyGGDQBHEkA2w85+5zznt/8n/799/WPPtBCHnhDoAQSz2hvG6k0YRigg4CqLOjvjFBCkk8StFLEYUirmRBHMYEUhGFEVVYobxFCgp9htb2gLA0iUiglOUKlFGctUAvw6NmDn6F9cX6GbRc1db1WrXN/hk4txD5caWYP5Sru37vN7/zOl3jjjW+RZhOCoAXCcZjOida+xpELQJiali6YkVEEziqqSiBLUGGt3KKlJNSSQEkkCqkVWipCHWCtxxqPqWotbYBAKsIj8MpxEhMGNZZ9NBxSmZJGu4F1AikEzVYtP7BPwAjCoIZIFgFVmWOMwtkKYzQ3336Dcjhg5dQ5pGyjpHxnAzxCjGhleQFbZBw/eZyHRZtOKyGQPYpxyrXb16moCFoJgbW0ETyxcoxmN2Yv77A3GhOEAdaAEYYGbSIdEgcho7KPbdeCSFpqqupwWNe+1ZfzMNi8T5FNeLhxG7xn5czTOFdDTLElaTohiCJymxFELaR/B3++zyXw3h+QvPYLuZs5wh8WH//Yx/nBz/8g3/rmNzm2ssLK8eOMJmOOLS/wkeeOkxpHa76HL0K6cwsUlaH3Iz/EX/3Zv8Z4NKo3Eu+YpFOSRsjO9hZ37txlUkxRGl7+yMvoKDmywEyzKdZY0vGY3/znXyLPDRvHlrjzkz9Kb2Hu4DczM5jrfmHeJ6SBmBV0e7Cx7XNKpJQHTlZH6eaPKlv7xfpZhzUjac18Z2D/HX5XAdC8Y6qMkPXnTP3PAxwIgfPywFPVo/BH/B4FVW3gogSR0gfGN9ZbcAJtLA2d0X9wk7D3AipKQEyoy+1hz/wdDg0wczt6f/HBFnLpkd4gvUUridSCWzevk2c1hfypZ8/gnGZcWsIoYjIZs7fpCcMQIWt6v5KKIAxoNZu1M00YzTQ+VE2C8OZIQpDHAa7eSJV8lxyrmFk/UdMFDhxJ6gc9Ww770vYzprVmc2uDL33p17hw/jvEkeT3v/5bfO6zP0Grfbgs57tDiJrcI2YPuqocoqpwTqK1JAz0TPUtwIaOKgxwriY4xIGmEWu0rgtZnpcYYzDGoGStx31YWFOv7GCmsDiZjGm0GihdbyxBEMw2u9qeTkhJbEzNBdCSoAoxVUmR77F95xa7G/eZTCacfOZ5ms12vXE6j1CHE5PMYEA/jLnaD0jimCAMWFtc59HtDSbZBFubGdJOYj587jl6nRY7022iJMSOarKSd57SFSy4RezE0F7u0XJNhumIKjAY646kVe6/TILaZKK0FZqSR7cv01taptGax5uSnUd3mQx20Doi8xWnn34BITRe1sXj3d+z/71eiAN2vjyiYKyvr7GxscF4PCaIQnYHI5JGAwMknWVOxgtcunKJJ5//EMvH1/jiF7/Ev/vX/wZlVbLT3+HBw4c0mg1W149z//5dvv71r1MUJdv9Ph6PFY6XP/oyb37vcA2cyXRCVRY0ooj/4hd+nj/8oz/l9YsXeO3111lZXabX7aKUOvDI9N4fcD/2m50gCDDGHRT8KIpQSh2Q/YAjC7mUCuSsgdl/TmK/odo3TRa1Vv7M4Fp4iZT7+u9i1jlTF27hUAj07LuMszjvjyzktaaLxeFx3szcngAsUkgSBXNBydXXv8Hq0wkrz3yEcvY3//wQs730HRaNP+zjf15O7/+j//+jGUcIHBKHs57rN2+zvn6CV199lSfOPkErCpCiy1e+8jV+96v/ChkYnjjxJB74/s99jsl0SlkUPHHuLJ1OhyiqRyz7I4Ws2AVhef75F4D3NnJVCpyXhN7jESjhcEIgXM202rfgEhKklEynGf/697/G9naf4XDAdEavzfOMVhRhkUyKnGfPfAzlxly79govPvcJ4sbhwvCjcY6rcpJIolsJytenhTI3VIWlmDiiIKLTDmi1Qua6TRrtCOEF3WYbX3nKqsALT9RsEccR3hryLKOcubcLIQjV4Xrk49GAOImRQtLpdfHeMx6O6MzNEWqN13bmJ7q/4YEUnkJrvHXcunaVS2+8Rv/RA+YXOwx3J1x89dsMRpscWzvN8sophKztBQ6L33vrT9BveFTg+fCzz3Hp4W28VsRC0263MdWYbDpkcaVLP8kYZQVlIdjNJ5S2xOYlzUaHhbkup26fJuwlPLj/kEW7wOrSIjfX7yEaml54+AYbVuWsMHhE6VhbXqYRGXa2t7nxxjd57oWP0t/eJM8t470Bt96+xYtnnyRN7jO3sgZa4mbELenBuAMjMZx3WCHwM3nTw+LuvXtooRgOh/xfX/wi6bQkjhXrp1dpH+/x6nfu4GTCXgErjR4//XN/i7CZ8OaF72GMYWFpnv7uLuk051d/9Z9z8+YNnJc02m3W19Y4fmKd3/zyV+Bw/gs3rl4lNwa7+wi7dZVR/xaj6YC//0v/K//7r/xT/tEv/zLD8YisTHni3FmajQYBiqIoa66k1sRRSFUVeGNIixzvPbEKkUHItMwIlSSUh58cO1rOmidVzzudr99XIfF+Nl5BzjRxaimESgLOzgp//RysE1D7ASFqyR6EAy3rJs8fkUcvDqlcPVpJZ6cQIWqjjQDF8OZtHt5/hdHt61y/8Q95ufxZzn3kCzMdlxlrfb/GUOf87tOqmFVw9z70mvbjAy3kVtRjgHoBO1qNDudfv8j6yknWjp9AiYIH97f5/X/9LapS0eu2Gaae7e1tjq095Bvf+AZPP/0kb127xXA45IknnqCRNLh1/Rpnz5zgR77waYzdP869d7iZhIajXhjepVgjSFQT4aG0JaPdXdbm5ymnKZdf/x4Xfvtf8WiaMrTlTOgmIE5CcuGpjECETbx0BGbK08+9QHt5gcoWQPSeeXgnKMuKQOqZKBJ4W89TnXc4ZygrT1l6qkpSGUNZVVjn0EJiXQXCE0aaIFRESYAzAoQjsMFMLN9hyyNcwYu6k4qiCCFrMS5TVUzGY8IwPDgOF2VBXhQ45+lvb3P36mXuXr/M1r27uGJEK5JoHeBESaczRxhGbD26T6Bj/Ex17rDIbEY7auGt5/b9u+SqqjskGxJM6pGRs4qJrcgmOwgsgYzANWg4QJYsyQ5iGCJNgNWWVqdBmAvKaooSjtIUcLgUDz7bQ8xe6k6vSW+uTZJouo0ec0sdqiLn9MlTXHz7GulkQoxgN88p37jE3vx9Fp84RWdxgcpX+HyKNLbWVncOZyq8Ewih0EfMAEfDMb1uj729PoO9PabTksWlNqsrpwjlEu0VSWNujqTd4uLVmzza2uQTH34R7xytZgspVP1+3LrF3du32BuMiZJ5Ti2t88lPfJqLly9x/cZ1njzz1OF59PfIcSSupBiPmWYplTV4r0nTnP/j//x1ut02N+5c5Qs/+gU++5nPkFtBlmU474ijAG0jbJWDrYeYeWWxxhBYSVoWeCXqzumQ0EoctAJCCJidYC37xta69nb1HukcEpBKoiQE3iOEp/QKITRSWIRQCC9rfSRf28h57IEp+ntF6QxeeISWtKWmco6KWg5aeMnZtQ7LT/4AF954BTMcEzt3MGLcDzsTIgttPcpx0iP2hdVmn9P/H7AoH2ghr3vxupsTQrGztcduf8SnP/VZXnv1NT7/+Ze5fvU8eGg0mnziY5/k2999k1Nnz3BnY4OwkbCyvs7lyxe5fes2bjZTMumE3U6tS6CVpCwOt1jb3tol8BAlEUkz5P79t3n1tTf50S/8VRqNBm+8/Sq33voeSxU0ypBpoDjT7eADyepCj8lkQlVVnDi1RuRKyhL2phU5JYtJh2aeIAkpq8M7LrygqhyVcpiq/nWEl7MZOUSJnCm6CoRWGO+orMV6hxAWtCMK6pl1RUE104jWBGjvcUWFEgHFEWqQxlgQM4q/rBXkvFKUZYXWGTgoihzrK0BQFTlf//Kvkw22aCaKSBRE3RglPJOqRMUBi6urdOePsbu7RTod4Jwgig83xS7LDCMjpBNMywKlBaKS+Ckk0QpinGFsidQKqTRFOaLZm8cXjip3tDsNxLbA7BqCeVXLAOuQKSm3b90nOdli4oeU1eEUfZ0oZtM0smGK1B0aSQMpIjb3RhzrNpBKsbi0RL63hVUShcelJTKY4h485N7tDZZOr6Gid4q4nl2sD8cTZBgTHyEvvLp6qlbNMxXeS4JAcO7JE8RJm9/+vW/y4U98guFoytZmn0k2RkhHHMU0Gh2mk5TJaMrtu7f45re+RZaOQSgWlk9y6uSTPPfsC/xv/+QfcufeHZ5/+oVD8xhPxlQ4nC+QjQbG1B0uzmCM4/yFCyx12+TjXR699Tbm3NOoRogochhPmOZTsipDmIxKJATzC0SLx3BRE2MqrCnIheCItwUnwAk3G03pWrtlNthQwuCsQ4oQj0KqmUaNkEj0zDdWEEpqn1yha5CCrDcE7z3Kmvre4ohG2Pl6BONwxL5W+Kw3FIHwFdPxJi7pkHQXYDej2erWo+R3SRgLIeopgFCElCg7rQEBQlOIGP++RBzeiQ+0kGshav9Ibwl0xGg0IUkaaB3y8OEmX/nNr7Cyss7f/a//JkEQ0O3NsbKgOHHiBJcvX+alp7u02oLFuWd4bm2Rdm+OV773XZI44PM/8DFCJXBeEx/hzHPzyldxu3ucff4lBo2TbNy7x+vnX+PUuedZWVnld//FP2Pv0TXKouBHf+zn2J4aMm1II8nnP/oRjq8c5xvf+AP6O32ePHOSE4sr3HiwyYPhNhNRsdOfkJcVJjhiJjwzkjBVrSSnpCYMApR2aK9ImiHeeQId0l2YR4eCcZYCAYEWJHFEnMT/L3NvEmtZdp3pfXvv05/b3/v6iHgRkZHRZZ9MthJFSSVLZqlUlmWXXHAZtgeGYRhGjWzDQ8Nzjw14UAWUXZZMGLZslqSSRIlMkmKXTGbHjMjI6F7f3r45/dnbg/MySA74HgcCoQUEIgK4g4Vzz1177bX+BqEA28WISkSppMBSlXiQNPrCkcbB/mO6vUrydWV5DSk1ZVFUXXiSYUyJlNX8+Wh3m/e/+w18McHyBO1OA1cqlK1RtuT4yQQZNOn2lghqDeJkfiYOZOFdIJQfNmrkcQJpicEhKH1W7A4rjSvIpEaqMmbxFJlXmu+W7TPbmXP9yi1wNhiP9kgnMzzbR+cJUmvGowmRyQnsJnKooBERxecf9JZ00LoAA+PJFF3mdOsepQj57jtb/JPf20TZihvPP4eKJ5R7fVaWl4jqJXI8R8/mxGXJaDhl6UoLacwZmorKvVIqkqzEtc7XFrl79zUODw+I4pRXXvkUQk3xgoIfvfMW+/tDfrPVIokLBsmAerPJ+sYGT7d3ydIFYVhjNp/xrb/9Jo+fPmVpbYVlq0lnaYN6o85sMePp08ekacTW9vliVXmWMk9jlG9TWpX4lSsVSE2uS5TJUUnECytLXLcKtt/8M9rrHXSZkZ4OKeIFNdfClZrjcYLVbNO8dI362gam2SNTFqWQFBd1oKKSBBb8RNsdBNLkuEpTaA1GYZDVnktW2jbyTDPIANIUWDIn1tVBgKlGXAaDZcqqmbpgRi6MxnGsSvMlqyR1pTBYKmc82GH30X0e3Y+5++qn6M8KSiPOhLyqfA1nxutIjCop4zmL/i5Baxmn0UXKahT693ZGLjEIU2KMxoiSOJkjheBb3/4GN29ex7UzarWQXq+D61YjiX/nNz5HnuXcuLpSiWjlJcLyUDGkZcYbn32pQhOsrFLkOUpZHB8ecv3W3Z+bx6+8fIP9t9+lqRO+/cGPGQ+OefmFl4kGx6SOA0IzKzNu3L2LDLqMt54wXcSsrV/h1t0X6Pf7HJ70ybKc9uplar1lwmmKPxlTJnPeee8t6sLmxs0b8B/9wc/NI4kWSKuCLsZpim3ZuJbAtR1QhlangY1AIwhbPvVGwPjxGMe28HwHyzJIV+L5PrnOieOkQq2U1cvoejY6KykueCN+/M53uXnndfwwQGLwg1rV55jKdi5LIxbTEU/uv8uDH30fypjnb15hNhqSxBF+6KJsg+249LoFxqsjpSLw6ly5fJPJdIQ21YF0XnheSFpEIARKW1yxV7nqXyOZST588gHKrtQtFRa5kZR6wWIR0w97uJYii0q0BssWaJGRi5xUxOAaltwmH3x4H+tFi8I5fyisVHWpzbKEk0XB4aTP1bZLs17iBjWEsDFaY4qMsNnAc21c2yBoUoQ90ukhtXpImeY4QYMkmmHZTgVBNIbCwHi+wLlAVC3w22xu1qg32ly7ts5kkbG01OHlF3+dNz4jqPsh1rrCCMM0ivnowSPuXlvn+PSU4ccfkaYJXuixceUSWTJhZW2DRqOLkILT02MGR0c0Gw22t86XjzVlSRLNqfkdPt7eJopiFILAdcjLDFnGdByPG60abZOTHPcZjrcJfReZZNhaY7THOE6YjyPUaI6exkz39unbHjd/9UsUSCxxfjlyLVW15UKgSoOSBltWw/C67zCbxmQ6pzBVsaxu7AWctTJaCyyjsUSKEs7ZOENjUSKNBvtMJO2C0YotAVOixNltGY1ShmjWp7/9gNl0zGqvy+raOtefuw5lii3PSDtnjbaR1T9sWSI8G3/lEsKtkSsfXxRIwc+gby6KX2ohPz455GBvF9dxKveM1RCjDdevb1bOQZb9zDnjk62/MRau5z6DJ3mBTYnEshLqdkDQbaCExGhNlmUopRgc7Z2bx//yZ99HaZd46yMGwzH5dMya1oTXc5alxap7hRsv36FuWzz6q++SxRHObMZ465CvPniCpxRX8gzjKu7/5b8lWO7iNzvo3adEgwM2OwGP/+T/pH9yxG+fU8jTdI4XumijudFucevKMm3fY7x9QikV8ZJPoQS27SJESWElXL66ynySMJqOCGoBxlJEkwmtVhNMymKaEKcxfsOF0KcoNFb9fDnM+WLGe+98B9+v8554G2yXMoqw84QkmpL0t7BdydLaKndeusHJ9ja24xK2WhhdUGu2QRekcczKcp0cGz+sISxQ0mZ57Qq242BdMCMfbs1wWwF2u8UX2s/zP/3Bf8fhoxP+96/9OX7oMx4uKIqEVrNHs9XGUMMNNbktWF3pEU4NhjklKVN3xiKIKa5qtKXp6yPW31ilP5ywmJ3v2WkUlFoTZRmPDmLCMOT6c89jPMkXP9XlhVdeI01SRv1jnn/ldbY+2mYyNqT9fXobm+jY4Wh3n7BXY+tdzbW7d8hlSZQnxFrjKJu6k+JcIFdfbzUpcsE/+kd/SFbu8qXf/Pe5cf0FHLvD8XCX9370NmkaMU8Nrc463XqLr/zxv2SxmKG1oNNeImzUEVrTrNWYDmeMB1t85zuPadYl690VFlHEPJ+em8fYGIapYnE4x+tt0F0fMZ49wbccLMviy6/e4NVrl4mGQ+azIZbjUJaKQjtYoYvUBceDMaOF5tH+FFuBfXBCu93gGEXj7qvUak3qzvkFVJUFWoMUCsfShDZ4skQqi9liSuBY+Carvrs4QVoWtkgohUtu1bF1iSlLQj0DWSMV4JiYdt6nsLosLAuMQJnzD3rHEmhhqsLsLLBMypOP3+fJvQ8xi5iV5VXy3GL34X26vuDRO9/i1c//WjV6OUPOlCLHlQW/fesqeZYwmE8r3kWRUa+3kEqeeRH8YvFLLeQPHtzn29/8Fr7rgc6feeP99d/8JVprXOk8c8f5xLYpDAO0Nvi+XxV520ZaNm0PLC/ACpo4toOSgnq9juM41C54IX7wYIsMQdNyeGEmuNzdoFP3aXkNvAX8zqufxXM8yjxl3a4zOjxgvrfPdDCkM4voWRbT0QkCQ+4E9Lw2wXxE9/E+YjZg3nSZ+Q7vRrNz81C2QipD3VG8ttalIzWj3V1meyMa65ewHZ80j0AX+J4LUuD6PtNpQhCE2LZLnp+ZR7gKN7dwAwfpCpzQRjkSS3kXysdKadE/OWYyeczxYMY8KbGN5nonoF1zadUsXE/hWdBqhuTdNmGjh4qnmDLDtmzKwtCflmTjOZaTspgMCcIGyqpGKsqyzxxafn7koxxRRESLCKf3KZrNBgPnFMuS+K6LtRGwt73F9u6H1Mc1HClZXl1HlR6PHj6k07JYbtjM04h0zWFYjDEyw7UshC2YJROkYxDlRVd4UHZlhO3bAt+pMM++75AtEhbTIXmm0XnB0dERc51y45UvMTzYx1q9RPb0CXH/iPFwwmZ7mafb+6xeWUVKG3RGWWqMkRfB6nEcCyUtup0lvKBGq7HG17/xdba299nee0o8nZBlES+88nlWVq7wrW++SRJN6XXabKxvsrZ6hTTPeLrziDKrEF+272Hbiv39Hdp2QBylLK/8fIQXQJYUlHmKU/OpNVtsXt1kcHyKLAXKGFzHIys14zghSTIajkecZMziCUaneLZNmmoWUUK35hKlGVGWMz0ecYrh7nxB6NcutFhzPEWWFhhTkGqBTksiCjBFBdNFUBY5Ak1epmTxiI6UOL6FsBQ2OcLE+MqQSIUoI7ysj2UpMiGwTIER4kKvXSRII5BaYKmU490n7H98j2g0IAzqhIHNeNhHipwP73/AZJ4RDw9pr6xSGCiFhTKGpdDGszMsqallhvlsUJlsFzZS2NgXGtP8JH6phbxWD/jiF7+I7/lQ5ghZrTe0NhXsLkrIsgwp5TNSgRCSyWTyTH87LzSmzPh49yEoD5wWRZ4jzxhks9mMP/yD3zs3j5gZHSfgN0STL9y4jFOvITTklkItMoTMSRYxSsBaq0FPOuRuE70ekc8nxIMT2kJBHNNbX8Z3A4r+EWZwRDbYY3RSogub4mr33Dw83wWRUfdtuibn5MePGM8NiXG592Cb1ZUm3YZPqT9Z9EBWFNiuc8bqFEgJeZ4jRGUa6/o2rnSxAoUUhjzKKC7Q35bSxmhBkWuirGAeJ1hCkGgPIyq9ctdzkEowHY5BOeTGpsTCtgW241Jow3tbQ17dqKFExrh/TKOziuv5WI7z7GA+L1QJJsqwpcWD7cf86MHb7Nx/wvj4CKktLNvjyqUrHO9vg4ZO2CCLI+q1FrYocGzFLJ+Q+QVzT5OmBSZLocxRuUU0X+Ccuauf+zzORitIReA7GFOwc3BAkYTUfZvJ8BTXDVjMx+webtNoBsz3d8kXcwLPI22FJLaNcn1ko4Gq18iNwmBwbbvym7QV5QWAdmEsLl/qIfWEN7/153z06GO+//a3SfMSWzrMTvZodzt88N5bbD19yMnJMZZjczoYYrTB81yOjo84Od5HChtIabUbuLLAEdAfDmg227RanXPzyNIEigi0IUkNrVbI9c1LHDzdw3FsjgZDXEswmUWYvEQFFSoljqJqZEFBnBUs4oRbl1c5HM1YzBSTJGOQLEjmEbE/R3rnlyNLGbQsKbWhFB6FMWhzZlBjKXKolpemwLIdHn18j/ryEvVanZQErVO8fIZ0XYSUBPmYtlpwqi5R5CWWzEixcdzz3w9RRDiWSwn0D3a5/8F7TAd9AtepDDR0xmTUZ29/m+l8jLACkiTCti10YSiNIXBtVts1lDJkeQGmpF4LiOOE0HfJ85w0Ph+k8DPP5hf+5N9BOI7Nmz98izzTdBp1knRf+qrHAAAgAElEQVSB1powDHEcB1dJXnvtNcIwxBjDkydPGA4mtFsdfD8gDAOOTvtoaVjrPEeUQiEalVWTrgr/8fHxhfjLeGeH32rcZHO5GktEUYTCQiEQpqTIYiyjEHmBziLIDbahgjRJF+vSZbx6BzGd0qg3SDHkGlA2kSkpllyy0Ma6dT7OTWPhKMFSo04RLUgzDW5IvdXimuvQH0boeUn3chOd52SZ5ORwwXA4x1EOoedQlClQMKq5OI6NkBaWY1euJ0VOqSXK/vkQSADLctBGEKclSZKBhkLAYJHQcgXad8lSzclJxNFkwjAp+He/tFw9ZyEr8lZSEi8SAr9BnkGWxCTxnCCsV2xQ27mY8KEMRZ7g4rE13OIrX/+/6aUBLd9h92DINB1y47lV5FKDLCpZ7TbZOdlnMpG0fJ9ockxSz1ArIXunhzi+jcwLpklKEIZIaWEr+8ICaoxAC0mrs8TV5T5GKEazKXuLEa+88DyWZbGI5oxnI3xbYbXqTI732No5JPdt6p0Wl166jbId8rpN2Gjh15uMR6eYUlQwOqOJ0/MPWGTJYHSCMTk723vYvs3163fZ2d+jyAriJCfMNK7SDPrHTMcjltfXaDYa6DRmf2+bx0+esra6zPbWIWlaVLcjoxFaEKcZYjYlTc+/sQmdYYscYywQEkvBUrfFYGcPU5ZM5gs+nM2IkgRfWaRGnpGDDLoomcUpcV4gpcHYilzCXJdMsoJJmmEriyJNSC6AH+ZZcka1lz9ZSoqzpeEnXfQZlluUhvVug05HsN4VjKIRk7Sk7mimBUgxJxRzhBWQFgJfxJRagnTOcOY/P6KTp4h6m/ki44ffeZPR6RGtmoelJGkSs78z5umTJ0TxHOlIGs0Wly5vUmpdOU9pjW8pTJEwGs0wSNBF5TaUxugiQxfF31/PzixLefDxxxzun3BlfYPT/jHGGHw/QGvNf/Vf/Kekacr7H3yAADzf54/++I/58pe/jHUG9/rqn/4ZUbrg97/8af7kq1/Dra1SFgVpGiOlZGN9nV/5/GfOzcMdLfDqFrGyyR7u4EuJrQ3KGFSZIeYLTFFQxAvEmWWXVh6ZrTDdNp3f/DzWwy303iGLg13Ei3expMPCc8k3VlgEU3alRVA/H26n0xghJcvtBrks0M0GQnisXF4iLArMQnB8MsHIjM5yC8cJSaISJT2MNpR5xZ5rNEKUsChzTZaVZEUBUYExIIXDYn6++bJUinmUEKUpZV5iCUFpYLYooANJnOLXG2jhMM1SnhzNODo4ZHm5RVFWDE9XSZ7vOVAk1YxUVgvBssjhjNl30bJTuJqmH5LLnMLL+e7WW/z6pTe4c+0u07nknfsPWCyGCCfFFpLp4hTLMlhOgVPXRHnK0vNL9F7b5N63tmiEbRxf0c9OAUkY1Cpop7jACrA02LYkDENevHWdOIqZRTWUEPTadR4/eoiR0KjXcEKfYf+U+pKkHdc5OH7CZvMW9WvL5MagkgiTL5jPwfF90qLAdX2yPK/gb+fE8fgJk0FM0/GByugzdGu06m2yLGMSNomSHC0ra0E3qHH71m3m0xGTYUkaZzRrLQQ2K8trPHr0hJ2tp3S6bYajKUo5DAdj8mJ4/vMoYgJX4TsOwjljPDfrBL7DbJoQZYo0iUjSnFZQg8msIt4UBXGSMI4ztFJQphzNF+yPxgzmKfNcs0gSPMdBlwWY88uRMPKMti4Rpng2b+ZMBkCe/d8CLKm5dWUFaWaYbIaczdGlhwghLgSuHlKzcnb1KjKPaKgJJyxhhLrQTzU6eUw+a3JwMmHvyQOa9Rq+66B1dXvZ3XpMEi0qs2pgY2MDW0Kps7MjxyCzlNkwZ/VSr9KK0pVGlFKCsihQEqLF/IJMfhK/XNRKAV/+3X+PRrNZXU+DGt1Ol/WNdRzbYXK8C0Lw6mc+h+OFxGnO//g/v8ra6hpCa9IkpbnUYz4asIhS/vCf/pcIKStCgJRIKdjd3WOxOL9w3bh7h+j2db793vvUeiGNMKRpubSVjZ+V1CZz7MmMcGRjTxck0Rj78X0y36bz+78D734X+adfpzg+5OBTr3L81pssjndxiym2Dw+WW/Qv1dj06ufmUXMMjtC0mjUal9pY12B3d8Cjkz6j4QzfqXGlHlJTTeZHJe/v3Gdl8xKOjMl1Sa5dao0uhdE8eHiM73s4boWEmM9jrl7d5PBw/xl1+ufFbD5nPp9iW5ICQ2BZWLqk7guadYdmw8V3MpQFn7/V4aXLTY4ePSKb1VleaTI0GZ6ruHOjA8rmdBBjNTyQBq0LFrPqh9244ArvrvlnBTQgSzVZoDh1cj7bWuJ/+G/+A/7oa3/GuyffpmhVpDIzcxAPXERTMryUkfg2xhuylARsNNoUCwFeE88vWUQjVrtLjIYDnAuG08rx0ECSa7zAJ3AE7brEFCXz4SGtdpeV9U2yNOFwdwvQmLLg0lqba07IIh7z9M0PcHyf5994DSE1ZTGiSBSlsUG4WNIluQDf/7Vvf4Xbtz9HqK5Tq0u++91vot/KsSyHer3HjRt3Weq1mEz7dLtdXLfGe++/T6fXwwm67O9sUQ8r/HtRJPT7p2ztbOM6ARuXNhkMTnFsl/XV82fkjhKEQZN6GOAFAqENstVAqZzvf+st+rMZcZYCgkWUkIymSCHPaOcltWadWy/c5dZLd/nKv/jXkMQYaRGhKDQIq6TUeaV5ck7kpgBdcS2EUPxEiaNqtkohcESJjgbsbN3DFhndThcZxAx3tjg6PGFx6RqyvUakNJntk8opyeQUZefomkTJOjo5/yYdnW7xZPeAJ7unuK7H+HhCNBni+QGT4YhkMcVSAjcIuHnnFiiXw8c/wnUEl3pLXF7f4Ctf/VOk0JzefJ4XX3wRk8V4nocdeEhKsiTl+GD33Dx+On655stC8+jRI0aziH/yB7/P6soypdYMh0M++ugjXrhzi9PjY957710WiznNep2XX3oFmReE9TpKKV546SWKNOPktM/p6QlZmqGTmDLPcXwfV1Wdw3mx2Vnjjdc+xcNHjxlP5kyHYz6ejcExNFsNbOEQdGoEl3vUnGVqcca129fxv/ch1tMdrOMdhnLO3maT/pOHPEhnXMoylkTJvCh5VItp+KDN+Xjl6dxgk/Lj9x/wxo0vsnbtEn67waMHe3i1Flq7pEnE4PgYr9ZitbPCeLiAPCbVJcNojO2McRwH3/eZzWPCsNKQGfbnlPkR4hO1t3Pit3/395mMxuzs7ACCrCyRBpLMMFtk9Ho1pOtQFjn5ZIjv+/iBoMwzZtMplq3w/Tp+rcY8ysiNxLOdii2nKzx6nqbE0floETdwcNsuli1xxwJ/GJAcwdvHH3Hlyh3eO/gh6SVDXreI4wJ8kA9hOp0SuB0ilUGcMhxNWYynSMsglIdTb5BIgykMZVqgnfNhfxXsvpIiEJZFkWvKLMe1BJYFnhcgBczHfUye4LouUlS+mkoplB1wvbPK6eEB+++/T2d9CbfRxLYd0igl0wXCklgXFK7bz7/Bg6fvUKwuGBxP6DR7rKx2OTk65OT4kGajyWx0gh9YDE9PaDS6jEdzrj7/Epc3LxHFEbtPH1IWhsPDQ4zQ1Jtt6rUOlmXjez6d9hJZdtEO5SdiTgKFZQkQhs3rNxgPF3zwww9ISHFrAdoobClot1o4rkOj0+T6jetc2ryCsC1W15YZ7e5hlGJRaISSz9QDxQXLcF1oKD9h5+uf1foSlYn5ZNxn7+G7ZJNjep0ak0XE4zSjE9RZuXqZvSfvM37wNpcuX2GRZJxMU5IkZaXX4shts3n7NXy/dm4eb390RJJNKYUmCOpYVkZRlAxOqnrkugGOI+n2akyHE3I95/qNLmHdpWs32d454HQ+5sW7tzgeDQi3n7LcbpHmleaRkRKjK2G/XzR+uTNyYdjeesprn/1VGq0W8+mIOE6IoojNS+vM45RFUv0YQsdi98kDvvv1b5JkKU7gY6Sg3e7SbHbAlFhSkCYx3UYTc2bOrJTF0/b5I4333n2PXmeZZDqn3Wqy0mmh8oIyzUnKnK3DPSaLBXKgadfsijn8mU/z+pV1oq/9BWZ3m74L6Txj5Lm8GdR54WhAu9QkRrIzE9ycGUbH51+NbFcRuAHHk4RFGpOfbuE3e7z4+k36+2P+9lv3eLpzgOUEhEFEza8hbZvhPGYS5wxnGa6rabRsZtEEYcEshThOSaOYND3i6pV1Av98+OFzz9/hN37nH/In/9dXUCfTZz8QJSX5mUpfWhgUitJkxHFMo9ciiRKiRYrnxYSei23Z5CiCVgsvDH9KHbJS/PtEHO3nhi7Q0sKSFvoYzIFGNSzKDnzn4+/SuOGRuRGLJCYpMrQsWWm18ZVV3a6LkiTL2T4+xQ9C0lwTJxM8f4lWe4nhwR7SSPILZo/ibFQhkEhpYzk+aZ4RJzOUpWjU62TJAl3mNJpNSi3IvZJ4McMWNqq0qHPKtTzjB7t9JqcT8o1l3FaTWlDDUFKa8kLCx9XV2wxHp+yfPKVA8eILL/P8jU3+zZ/+v4DBdXyydIFZRBzs73PpskWa5UxnCy5tbFK88TlODg/YfvIEg8b1fErjVEbfZUlQq+OFIf4FoyalrGeFXEkLKWWl9CcFG9eu8cEP3qfb7vLy597ACWpkadUJGwFe3cd2K8RVWZZce+4aNa2J84LByWlVgC1FmRXPEGs/L1JdIvSZsN0Zp/PZgYvACMPezg7f+qu/xjYxRRIhZUGZG5qNDlc3N5mMBxVJLJYIM+N0ljCdRwyGLZ5GClVb4/nnV87NI4tGaGOh0eR5NcKMoogizwHN8vISm1cv0ej4ZKnFdLrgZDSlIxp01mxSB1bWlmk264yTlMHpKcOTCtOPENSnDZSENLng9/JT8Ust5MlsxD/7p/8h9e4aTz++R5nMCYIAXZZIpZgmJbPxiJorQbk468vks5z+cIAuClCSw71dGo02viXJ05jp4IRseIopCp6/eZMkTdnfOp+pZrmC9z74GMtySDT0s4iG4xC4ITUN+d4+QaODmyUEjqHwBUfzEeaNO+i/+DdMR6fUsQgx/PnNGqLWYzxZMIky4lLSn2eEuwOOjs6/OnsB+J7CMR5J6VLzQpKyoJAltRtd/vHmP+Zvv/Zdjg6GlElK086w/IJup8FwoRE7Y/YPj5mNp6RSs3ZpnePTfYSATquBLg3j8ZzZ9PypX55nvPjK6xwfHbF3cEwj9EniBN8SKMclSzKMBikktrTwagFYLvE8IY0yiiAjTzK8pl+NZlo9wnodS1koZWOo2HPlBe7kjq0waYatPXrZKqohuH7nCl7T4sQ+5MqddTgtCDOLUsHpwRH1VoClXcZJSqFjLEeT6IIgbKEXGaYocBG4lssgytBlgfDOfx5FWVaKmKrSh1HawbZckmSGbTlMx0MQ4Hg+ju1SFBqTRDiuh5I2Eodw7QbWXLE2n2GkjWh0OZ4OOB2PsButSvHvgqVasci4s/Eab330TWbZhEI3mC8WnPbHWHaI6wakWcz+/jFaV2NFr9Em9B0O9/aZTCKuP3eHhx89QItK7tl1KsloS0lKIQkaNfQFqCbLUmciXxopFFobSg0IgzyTNA4Cn5X1NSzPP+tKXfIix8hqsauEjRKwcWmD8ZOnTOcpBo3tOHwi36ovWIZ/fLyNMgolFJb4ZJx6pl4qJMqyoeYiA5+jvSPS8RiSISiXg4MT7j/4GBk4BI7F4WBcLVk15IVmb39C7bmb1FotsvL83+2LN1a492hG4HvEcUyWZWRZVu0pfIdbt28Q1AKwBb31NfSTbd57811CJej+wW/z2su3WW43UJbi0AiuXrnMyckRYRiilAQsovmMxeL8G+zPfEe/8Cf/DuLh/Q+IhM9olqCzBaJIsG0Hx3HI84zXP/dFFtmMxfAEXWTsbu+S5xJpNDrPWV9d45/98/8c7XlYOuFob5vdrccoXYIxrK+tM18scJ3zr2i3XlpBxw2yKGM0nVGUJZFTkKicebSgNILpdMZGq87yWoNas8XB8YT3P77PczrDBiQlBYZhq1dBi0KbaKH4gatRnmESz5H5+Z1OoQuKUiIN/PX3P+Izr93k5s3ncHt1jsZDxHzM5lKdIE9JEui2a7z+pc+QSYFRHkeDOY8fH3JwPOVr33yHZLHAkWAphW9ZWJZgPJ5RXNCBFnmGkJI3PvsF3vrOm7iWIo5iBAbbd9BCIv06UkosRyEsgQSUkhRpAWkJSYbdcZnMR3TCSqY0COpYjoPtOEipLkLnUuqcPMqx7CYb3jLFkqZfnDB4dEi5rvnUa6/zYvsm3XaPhIQPf/wBoQgojsZoabALqDebhN02g/05pRHYlmI6OsXoIWjDYh5Td9sX5KEr2WQpKYsMaYpKiMxxKEpDliwIayFOUENaHiQpKs+xbAepbBA+V3/jS0w3HrD58B6nGqbDPsvtgEEaMR6eUOuuoMsLZvWmoN1q0QnbPH9ng+fWN7n/wUeUpaBWa1FkMa1Gjf5RJWd8enzCjU6X0HN5+4dvk+uS27euEzaaDPsD6nUP13FJspSszHE8j06nxZPHFzQ+sjJnqObSVResrApOaSsLISudFGMEWlfPDwlCmjOZiE+UChWtlSWcXpfR3gGUJc1GE0sKkAoukJ/emhwgS4EwlWYKZzLTnI2ohJAoNKu/8mnqp5eJJhN4dvgApaauJDVbkBaiYnUKXeHO85L00lV2kwnHRwvg+Z+bh20rJJpWvcnhdPhMhrfRqPH5L3yW1157nSxPccKAOAOWE44/fIydZnSM5FqrxdVWj1TBrY3L1GoByY1NoDKTiKOc/ukJW48fnPs8fuY7+oU/+XcQFgUtT1FzAiytcK1KTlRKyWg0YufHP+De/ftcvXqNQmp8V3HrxVe49+77HO3scXP9EpvdHt947y16dRdP5Ny+ukaj5iOEqJx1ZOdCHOj79/6WzZUX6NavcnA4Y3RyTD3w8QOfsFEn9HySvGA0HLO80uLO9VvMi8fsPPqQ2uiUDcEzU4CVu68yefg+0XTM47rNO26GFyoKoTEXdBjDcYS37BDnBbvjmMUP7vHxwwNu3L1Ob7XNdP+A0fCIRruDV4YUtiENWoSdJeIsZq3X4+pLL5PnFps377CzvcNoNGYxn6PLkuWVJcajMcacr3GSZSnlGQx08+oV+sdHBIGHlBXVOUfhChs3rOHX6sgyIZuPCeoBujDkcQaForV+je1vf4S0PWq1Gp4XYNkOfq3qzi+cgVrVnNMzPok9J67nTJIRk8kpzXaNPI8JvR4r9WVSa4G6c5u9g0PKo8qooOb7BLWQeRpTGgutC4TMESJDG6i1fMJmSNg4v5A7gV+NhaTEUhamSMnL9ExJz8Z1K0MUZdlIy8FyQCYxrhtQ5po8t1AthyQdoddX2Vy/xNM3v4EWCZ1um939e2B5hI3zeQZJNMHkCQ/fv8cLn77Npz/7KQQ5UTpmOo3JigWvvvoZet0eT58+wXFtjvf3eHDvHsoJ6K2uomybjcuXiWYJ0ghmsylu4BGEPteeew4pDPPZ+SNAKeSZKqbzTP7B89xKQ8dU5CkNlKXGVRaFslHSQosSoSvNf2NEpTroWWzcvsP2kz1mwz71wEdSHRbiAq0VYwlKo5EI9NlBIgClKhOLsswqUb5mnbBZI9AGS9kIw5mxR2W0kekCadmYsqSkrIq9AWUEu6ND7Ave04+fHlesZeM/c0UCuHzlMjduXCeKpkwmU+70Xsa2NEGry1zcZ8kVhIFCKQNpQSEVvWaDpEgpyqwymVGKRqNJu9Xk/o/fOzePn45faiF/6eZVXN9HSYGkwJzhvSu3mR7zRcZKp8M8LdjZ3saXgr/5i6/S8OusdOu8//4P+N73vkFmSl596RrNWkizUWPWqDDmrVYL6ThoeX4BPTqc8+jjr1PqgC//1h+y0q4zHA4YDgfs7BxTzmOUbRO4AYcP90mmKdP9ba4MJnhxSWoEKZL8znNYVk6jGbCQy5z+eovXyPjg3ROiyCAveDF73RZxknP18iqz6YKTRc6MOYN7T1DvJbx+6zYnWvLg3kdsXLlBf2/G93f/hjxPmM0nhLUQywmIFyWzfp9rV9botDxarSauG/D44T4nx0NajfN3BkGtzngyZDoe8tLrn+XdH36f2XiAZSnKPGe3P8UMEtaXC9o9hUHTcVyagU9neYkgaKFNyY8fPGG4yPj0UpdavYHjB5SmJFks8ILwQtRK0GqQxwvKyDBcmeFcc1lz1uhu9Nh/tMXzyzfZWLnMx1sPSMQM6dvE3pAiLAhqLZw8QzkhJp4wmQ5Z7q2SZYJ4scCyLVbWulhKYl+w/K2w3gpTlJSujbQa1Ns+RTJBKYgXM9I0Q6lJRRoKGmghsJyQYjRA/ug9jh4eoW2H7hd+hZkA/5XnkMUEOy1ZXV7hye4Wwh2cm0ezExA4Fsv1Oua04L/95/89s2TC6toSYcMjWKnzo613cGXIwfgUo0uiNKHVrlFqzbtvf52jg2VWem0eGE1OStCpI5Uiihdsblzh0cPHcEHD4fs+1plSY6PRIE1TkiRBG0Oj1SRHY7KUKEto+10sW5DnJdYZhb/UGmEEujQYKbh8+QrP3Xie/jtTbt26WZEAhcCyzuc7SCNRlkQizpyXOCvm5TPzE1sIznwkQFKRhrRGSomRlfa35fgVpvuMpGaMqcZpxuBojZDnz+r/k//4P+Pg6Jh//Udfwaspbt64STRdkKcR33jzr1laXiJPMz768H1cz6N/fMzt28/hbmxgL/VIjUZYQJGTqwrd8/DeI+7dv4/neXz+M69TFAXzyeTcPH46frkOQUoidF6dnkaficFryrJahh0cHPPOR0/ZP51yaaVFNJ1w5+oGIKp5mJJ4lkUzdFnqtfGDgCAIcBzr7I9XuQhdoCrXbdSIZiWLKCfP5rTra3iOotltMzjuk89PMFoTz1OOx0ecTCeMPnifWZRgux71tWuIy2tsrbjs7u5w+co1Pn70Fq6AeqfGcjfmKI8uJCbZyib0fMrSUCQZeV7QcALKuKIz//jBNvt7hzSWOnRXWszjBUu9DscnB6yvbxDWfE4HC4wuWL25TrvhIVyNEYo0STg6OqLR6BK2z4dBDvontNpdpBCcHh9jez6W42I7Fl5Q47g/ZTgaUaYLTk/7oBx++3d+k3R6AnlEPB0iXI+wvcHlzU2QNsp2aXZ6KGWxmI+ZL8YXduRlUpJMUvrzEVeuX0c0BKFUOKvQzpfZ3dvnsH/I0fAAUbOJTclpf5trrUsI5RA4beqtJtPFlLDmIKwCW9p0nDWM1uRZjuV7aH1+HvM8RRUaSwnILZQRFJS4VoV3FrL6W0lDlswokoTCb+GEAcu9dQ67EcPlJeplzN4f/R/4b9yFMCYrUmzLw/Zs6rWA9ALdfGEp3MDl1Vde4U//nz9hMpjht7q06s+xWMzYPz5inkaUC0Wa5BRJjrRtfvijH3Lt2iZhaHFyuM2wP8dIjXIdSlEtn0PPx3cDkijlxo0b5+bhedWNrihK4jihKHK0NhWjOLQrnfw0pTQVucxxbJLJQbU7IAQUSgmyPEcqiSUUyjI0mgFLK220iUEoFtFFcss5GnCkhX1m3GGMqSDIZVk5iWlDeab/Xmpd7Qaobv0/bbtXao02lZ5TWRQ/wzq+yFrzR+98m5PhBC+UWIHHfD7Ati1AMJ9X2jwCSZoZlILcWOQlTKOYfn9UyTNIied5hEHA7u4+/+pf/W9kWV55KeiYz3/+c0jr76mMrTZQ5gWgSdMEXZpnhgVlUTIanND0LdRKi1devEW39gp2WMNzXSzLxrIUUlSen5+cwFprtCmBn/JIvIAhduvWBos0IspGvPntv+Fwd5urly+xsrZOs9ngbz/4Nu12nUa9xfpKj8BpsFiyaNku7ZUrTCYx9wanPOg/4h988bfonwxQbkng1RC5pterM5lmRNH5y72d7T6NWsBsnHBlfZnxcIzSAlvY9Ocphpjc8lhaXSbL5wShYLY4wXYEYeihJJgyIfAURi9oNGpou0BaHrEFG5dWWVtbZ34BsaDd7dFudWg0GsRRxbYdnJ7ieT5LS8tcvrbF0ydPsE2FCvrMF77EZ77wBQ62HvLxh9+DPCNorFDvrvBf/9rvEM2nDAcnSClwXBch2uRFRnKBTrxjHErlEdR8eis9klZCMZ8SdJo0ZY+3P3qHXqtOSUmROoziBapeR0sPjeD0+JTD5BjXUyyvraAsQRwvsISHa/tE0ZSsKMgvcOaRWaXnoVyFaxRSaCxlkJZNVpQYoZC2ixYxaZ5VrqxeSVEWjKYx3muv0tm8xt43/grLzJkePiRbDnBrLTzXQyhJo1bj0ubVc/PIM814MOfa5nXq3R7LcUqBJMtyBidjnJrBEi7pLEKhCJotXFtRpClSlliqArBZtTq+dHFcF1v5hK6PZzscHpxSC5r82q9+7tw8PtHrFkKTZ1H1b6UQSpPplNXlZeKiAJMhZYmkxFE5aRmTxGkl1wwoqbC0C5ZDSUq9ZuPaObPZEZZtV9LB54TtOIhSg4GyKNFnB6Euf2I2rp/p9QuEEliy0sn5xEMVfuIparSuOnEqpNIn8iAXGaBkOsYLFSsbPdK8xHUsfu93/yG+V+NP/r9/y/b2LkraRIsCEMxmM954/VXefecDtrf38X2PVquF7wdcu3aN0WhMvz+g1+symUw46Q/48KP77B3tnP+9/FT8clErWYHWOaXOz/z8qoeZ5SWj8Zilbot2VxLWm/S6beq+i7J/Mu8Wojp9NZK8rB6SlAKBOoP+Vua25oJOuNVxuXF7BW3l3H9/m+PjfR48XuL5G7fYvHKd7/3wx9RCj1oY0Ou0add8eldWiURGUZ7weLzPR4+f8MLNW7z22qf5F//yf8UJLaRdbdT9wMLzJcn5dYs8NQySCa1mnaIo8QIXbEOUL/BCF9fzGI5mLBYJ2igMBa4boEtBmWtMoSnSkizPuXnzMlkyJs8lWQlHBxPW1ldpNFzm04sNUq8AACAASURBVONz81hbu0xZVAa4Ydjg7ouvUKs18D0PpRQvvvwS08mY0bBPs9ni7kuvEUczgmaXm6/8GoHvY7TBcV0azSaO4xLW6gxOTxFedW0NwwZecH4HKlPFxtI6KpNnM8+Co/4R9UaGdlym6QIm1WJ2OtIYx6FRb9OfRdiiJKgH7A72aPeaGMyZx6kh8H2U9CjJiaIF2QW4v82lDkqKyif17BqPMCRF9T0UhSDXGbYSWFYdzw/IlCSJI46P+8hxxqPv/ZDiaJ+Vl6+Ap7Fcm6DRpkwLHNfGr9VotZfOzWN1ZYV0Nufejz9kfXOTR3t7zGZj5os5oVcjnsQEgYdn+bTaXVYvbzCbniINpOmCwWBCqQWeV6fdqlXSwm4Nneb0uj3yrGBtbe1Zx/3zIilmZGmKLFOuXlojL0viLEWbjIIFV9Y79IdjKMcI02c+mWORUZYRs0VydhCUBIHLfCzILQ/LNXiOpEynLGYLPN+neYFKJ0IglERSqZ2Ks5u61holBGmaVnK1Z9+vVD9xYfqkydNaV+OYT9pu+RP8eqW2enEXbDkh0uSUekYSp7xw5w6379xkdeUSH957woOPHuK6HsbYFWLHstjZ2WU6W+A4HmlSYc2Hwwn9/hDPdQHDrVu3UErxeOspYaOFewGe/Wdy+oU/+XcQ0rIw5SfuN8FZATY0hGB5dZ2G71GWBbPpDFsZgrAqEHCmzWt05XQtLaSlnhmU6rMTv9QVxOqiu5HJSxqBze2blznZmxHHOZPohLffH/DDd39AZiT9UcRgELG728cOLZa3D/BcG9/1yIXAxaYXLPGXf/HXnPT3WL5mk6UJthfguj6NRp3ZdHxuHrZjY7TEsh3Gsym9pQ7YEuXadBshi1FG4LhgChzHQ+tqiRjHE5KkckBJ4hQ/rDObRUwmU5IShAwx2qHUmqyIcWrn31DE2W0myzJWVlephTWUOmNPakMSW/ieT+Pqc6xduoJtu+ycPsbzfVbX1knThCSKCOsN8ixFWTZhvU4cR2RZhuu5pFlGWDv/xfRlgCd9ZsUUUsHR3jFFVjCbzfFbLl5Ypywz5vM5sRC0woCsyDhJIgI7oNlpYtX/f/bePMjy67rv+9x7f+tb+/XeMz3dMz0rZrCTAAiS4E6KlEibslSyIjm2rCRyJCVOZMcVVypSyUkqcaqSyC4nViKXVyqmihQdrZQpioIoEMRKAMQymH3t6X15+/tt9978cV83hgzRPXRUKKtqTtXUTHe97rnv/X6/c88957v45DYF6ViDUkUgPXJduM1fSkK5923v+9KhLHDuMVJ4btBXCHSWklDgK/ACjzgso4Fmd0BhJVk8Si81GN+jdGiOFRUyWXYMWWM13X6HwAsR1TKGvYfylWoZkyV4oWJh4Qivn32DZrtNo16nFJVZXx/Q6/QZmxinHFeIvIjlTociS+n1+rTbA9KkYHSszOjEGEEQMOhnlKtljhw5xuKtWzRG6vvC3GyxiRCWMLRYvUGj1kC0MygyEJp+0iGMBelgm1YzpLnZYnqsgdEpadbDD0KsgeXlTQY9CPyYscmDrK62aW4NyPI+YRiSjO3tpdrLEgIknpDuZG4tVgNYJyoH+MoZMxfGkOsCIRV6mMTBIcUArHGFYGG189u0ID2xa4K+V7zxxg2S3NBPcySWMKgw2hgn8EN8P9x13ApjH6U8Up2yeGsJT0q2tzsYo8kyTRgGtG2HIPBZWFjgJ37iJwD4Gz/3txkZmcDbB313e7yjiTwqVYYO9noI/n/rCCOFQHs+kdSU44B+VnDp1hoHpyec0PvucUeAlMMptNtthVJOV2Q4pLD7AN1GatNE4Qilbocf/vR76aYDtrtNtNbkBVy8tEq3ndDvpFitSBNLPGY4dXqGfrfD2tIm2grOXv86jekKh89YlISNtYRL6+t0exlaM1Sce/socstgUCBVSqVeYXF5C4umUooYa1Qo+jm1epXxyQb9XsHli+ucOVMlHWR0265PmWmJSAuWtxIGfY92JyEMc3xfIvpb1BvjhMHePfIsz0DAyEiDMHQGCHnujpl5luH5EQfnpymVSvR6bbY2V5k9tIBSgs21NQpdUB1pUK7W6LZaGFPQ7/eZPnSEzbUVqvVRtjbW6OyDjhhvTLJ26yZxI8ATkunKBPGhAyxtrNDf7lCKynTaBbnwh9V1F9/L0YElp4foDvC8MnmhIJRkmUYUHn2dg4DNrS1GajXUPlVXO3F2YIJ8mAgy+v01ssE2RigSOUutKrGsk2cJ270+lYMP45dGOeBFBDglybwo6AyaJNtXWF2/QZqsk6QpjclZpmeO4u/DMP2DL3+FB+67l6On7uErX/kD/uJnPs3T33yGLMvJC0OvW2bhyAJh7HNraYXlm4tcv3ydNMsZDFLGRieYmRrDUz79VhdVrTI+OsqJU6foD/pMjjc4MneAN86+vuc63n3iKFvtPkYXTE2O4UmPRlxlY22d3CjGpo6zvLzC2mJCrazptAW6XyCET+RN4hU+WhsawRhBtk0QlVm5tUZhBGO1cSLfGTSPjOxtVh75AaGQSIMzlxAO5yKNwBhNXxiyPCdQHlJIdpiRb/XHXeXuQmAFmAKKvMBiyYd6J57Yu7XS66UEgWKqUWZirM65V7/N//DLFym04ebiMr4nqJdDphslJqfqjI2X0bogzzRhFKKkZFAobiyusL3dpJ9kPPr44/SLHtVaTHd7i+effpqZA3ujq26Pd7xHLqQY+u0xZGYxNLGTFCgyowmlBOUxOjnDVnubRsO9oV12mVKYYU9MAFi1OzCF/RO5STNGoio1v8K19eugCsbGKoAg14ZavUy73afbSeh2BvT7GceOjVKtFlTrVRrjEWmWEQc+MnLaxEUG3X6KLrxd82et9zumSaLYI8uGzj6ZpVap4iHY3uwwNzsLFja2OxS5oj/IicMSgfTIrUEo5wDUH+T0Bzl5odlqpcSxYHZ2jLGGpF4ps7m+98kgCGN8XxGGEUo4TWenHS4oREF9pEGpXB4iBCyeVJTKFTrNLbIsJYhionIVz/MJophep0maJMTlKvXGOEEQUa3VSdbW9lyH9grCOCSIAnqqTafXJA8j/DjEGk0p8hySZrtFnnZgqLKXZgXWuOIgjgI8WWZ9Y5XQ9ygrp2dfGI2nfDqdHsruXQknxm1mnlJ4vo8QhlJQojwy7eBrskQgBbazhbCGcmWUiak5sAJhC6ezXyiSXoo2ggxBsz1ACI9c+xyYPUwQlQjivdFE589fZqQ+ykc/8mEOHTrIsaNHGWuMsbG5xauvv0G71UMKSRgGHD96mAsXLuJ7AWlmsSaj1+vjeT7luITv+3R7HaJSTG/Q4eVXXuG+4ydYW1vl1MmTe64jljVOHDmGtZYgKuF7gk5rEzuqKGzB1EyLfrLN/NwctdESCE3Zj5HSsjB/iDAM6A+6+J6i352kMILRepmpiRGOzE1TK0dIJWmM7J24+jpHDxO5kq6KVkKihGN1hn6AMKCLnELrXYNMY7Qr8qzAE86mudBDroDnoXDDzzQfoJSbw+0V9x6fJy4FlMohpXLMYDDK4uIiOs9YmJ1gcmqKSqlEOQpR3rA6D32EUFhrMMZSo2B89CB5NsnqRodLF97k9377d5g9NEeeNlk4ssB995/acx23xzuayJEeu4g8YbHiLfihk6c0FMZitEEID1/ASrtDgcfY2Bi2SPF3qm8khp2f+67+1j6U4/nZOYrUTeAbIyPk3RzlCbACKQ3KS4nLZaanayRpTpomKKFdq8FKlCcYKcdEQUSn30PnBYXGbUjS4HmKwSBjP4OPStVhcZWSDgOrJRvLPTzfUip7XL22jOd7HD8zTzkuoTyP5VvXaNRH8DzL5Vtr30GfLowgKXLqpTpZkVCtTGFz6Lf3btYL4eEujCTLM0xeDLkZDrbl+4GbQVjQhQXhkSUprVaLvDAESAcvMxZrLHlWMBgkyO2WcwbyJdIL9u0/6iAjHg2xgWUlWXSGwzZGGInRBZubWwR+mVJcxkhNmqZo7ezolOeBVRR5jhdprDbuGB1KDIJcW8IgpMg1Nt8b5lYfnaEoCrchi2zo/ei5w6ASVFRAlgxYublJSErcmEBYd1SWMiKIXPIEMDonXZgne+QJgjB0bu9S0E9TeuneRK3VtXWeefZZkjTl0sUrpLlldWWNoihod7tcuHiBW0tLHDk6R7vdZtAfsLa2iVQ+RaFptVqkaerMCuwSAsHc3DwbW2ssrSwTYCiKgsPz83uuQymJJ1zVGnuWNO3T67Qc09fzuOfwBDWVMzo2RhAHDNodpmamGK/XKYUSnWeEMiSKYvxRHyUVk42Yfp4RxyXGG6PseqDtEdtZH2UsHpKS8odJXDpZYgsV7bvWipBIiUPEFXp3uOl7Ck8IrLF40uUPd7J3LcRw6PWr9qm/js5NgnC4dSsKqpWQe04tgLZ4QuJ5yiFpjCvmlFRDz1brtHiUBG0IhCAOFeFMmUEK33jySbA+p08f5sTJeeJoPwrdWyHupLl/N+7G3bgbd+Pf37jzbvrduBt3427cjX8v424ivxt3427cjT/ncTeR3427cTfuxp/zuJvI78bduBt34895vKOolW8+/5zdwX4bJXa1hHf+yCHDyhjzHd/fcRBx6BYxpAuL3dft/IwZGjBrrfngo4+/7cjXdK8NZ7wWXRQUWr+lhZxDmucUWKeOliZkWUaSpGjkUG9iQJIkSAODdko/Lci0oZvnJFnmHLCTlCxJ+aW//z++7Tr+8T/5azazBYMsozdIwDpyqjXObb0xUiFQjvxgtUEKS9nTKE8glEL6PlZKKAxJL3V+nanBGlC+IqgEGCmwFn76P/znb7uOI0fmLcL9v1IpJ19rLLrbRff6eBpKY6Pc977HCapVNja3eeVrX3aErCHZQvkhjek5Jmdn8cKAN8+/SXPbwR7TNN1l1aVp+rbrGJ2LrNaZkz/1y0zOPuD0PNKUahgQj0qEjFnb3KYURrQ2tggqkk6vQ5booXbINoEvCaJRPD/gwx95H5evvsHZc2epxFUa9Ul07vPik8++7Tp+4OSDtnB6wIRCOg9HAdo6jaDCOrSHlJKjx45itKG5ucX29jZKKRYWFtjY2ODmzUWSJN1FViEcLsM9AwLPGN7cXN0LmmDvDIzgcHY74lD/LiHE22PufuQ/+kXr+x5CSLabTRaXlxjkmWNWeh7hWIPJkXFG63V8MlrdFq10COJSiiiKqJZDGpUSDWkcO1tYpE0ZaE0nEwwGORsb2/zRv/pf33Ydn/x7N22BouobUlshiEL8OED4ilwm+CWLHyhH6NEKMklJp3iRwiqJsBqdDSiyhF7SBsDzPKqRxGxe5vLLX+XGxdcwKC5947ffdh2/+L+ftUJohDBIPIJ8jWoU0KGCfguWx46UjjNYEd8JyhEOxWKMu3Y7CD4s5NIOX2r5+z//wB1BV95ZZudtN5lQcvfr707ot79+B1YoxG2KZ0MK/u2/z4H+3c/vKLW9XVx77WUnmqMdaaMocrIsd7j2wsN4HrnRFMaS5RblKQaDhCQvyNIcYw1pmiIQrG+2SPKCXFvSwtDpdknTlDRzFlJ7RZrn5KbAFAW+kC5JDHUjCjP82rpjk8WisBQ7ErpGO6v7YZLPCkcSGVKkMEaT5wVaOLPavSKOHRxPGz30WhQorfGikCzPUEbgRQFBFBKXSjS0pRSVKYpiiN23VKp1KmVHfNheb1EUjvxgjXX6F7ex694uxJBNaTXge86suTBsNZtkYcCh6RradqiNRrSbLXp5i8bIIQZZwqDZRxgfXysQlkHaoh6Ns3xrhXSQUfJLNCaqNEZGWFvp77mOJJaY0EdIia8UGoNBUIDzSs0LUlMwe3CG6oEZKpUKp0plzp07R5qmVKenmT55jJtf/jL9Itl93ztFidPssvuhZL+PEIDB2gSIh1//WYZACEeqSQYDkjRBWwerA0EcKBrViLFKgC8ERge0ssyxrnf0j9DkJmcltSgM5VASepJcG6QAkQ8o+/t4dlZDBgXkJsOEhiAskKpAYPDKFfxIgBmATimMxeYFmyuX6WwsobOUPOsiRcra8lV6K6+ijYMnCmDQ69HbXETqBC32Yf5K9zyC02gPgwpx7NFPxTAB70ApHdPcIjBiKPA13NEtFqxFSjvk0bh7w4ITHPk+L+E7nsh3bmozxI5/dwXxvb63831gN9nvVPa7GPTbNoD9Esb69asY7aoqPbQj01oPsZ4xBD6FNWTWI7UhQgiKomCQpQwSV6HneY4Vgq1+j81Wlyw3YAVZltHr9TDWubrvFVnhNgUBTlzfWIy0wwtvHfHVw+GzhSMwGOlUCOyQ6OBkQCwGg7YabXEynEoOdW3czbZXlKIQy44A2bCSkgJViRE2hxziaoVytUq5VsX3A+YPL7C9vU2v10MpxeT0DCmKNE3pdNp4nucs3nAYb1vsLYgETpPaaIE2Bb4fIa3EQ2B1ziDRdHuC6YMT9LuWa1dW0KYgzRLazRahkExUR8jzmG7Wo6MHNFtNzp29SBw7QS4p3e/udNp7rsOXHiinsFeRHkZqCguJMQitsEaAD1J6DPoDOu0O+dg4W9vbSClZW9+ge+0a3X4Pu/PgWouwLnk7qpXYVebbK/YolN19bi3poE9/0CYqKUqlEn/WidwY7eQJtKbX75NluTM38hyOu+QrGqWARuiIOrpaYrlTDKvN4e/QOcmgQOcSTxqUMWgBhQhoNpv0N1e4cW1xz3VUgnFKnsbP2rTaawibUuQDfM/ilxRGb7GycoXlG+dJ9SYi12zfOMdgfZnAGDxp8JWmyAekUYk0TXdP+0EQElMgpaHYxzFJCjuk8ksQAg+FlCCVq9CHBnTDpO20XfQwobvcNGQq3fbH7vxth9IQ32e8s4Sg7xG3J+IdlbKdo6gcfv2dr5Pfkah3vv/df/aKxtQhitwlltWtNisrK3Q6HbQ2PHD/YVKt8aVAaEmeCgqtaba6GGkppGB5Y4MLFy9QH6mzurnJxMxBkqJAoZCepJf0GCR9rNhbZa/X7yOEkycwhSY1ORqNkBCIEs1NiEKPOCrhBQJNkxwLQmKFcPu6MVijyYyr1q0C4Vm8wJKmhjwtEPuYuNZKgROZEhKkckYApsDqgpHxCr4sEVZrbLS26RcF5SDm9COPIKSk0+7QarWo1mos3Vrh5uoyxgyvp+fhec7jMc/zfY/9HhKhAgwSbIQoBHm7x1gUUh8dIxQh2XrCzQuX8IsCKWDj8g1q0ol1WSz1uEY1qpP0+hTa0NlOyNs5B0cn0M2I82evEQR7E4JGSxWCUkw5ipmKY3Kb0s003cLQyTWbgz6ZzFjf2GZlZR1jLf1sSE7SxfD9g8Zi/bfuaatdMt/lNO8j7vadsXNP30aiMznPPfMkigHV+ihLt9b56Cd/5P/7U99V6Hy/ce7cm1hjkEqxsbmJkQLle1grqNZHmB6tUpEZdaGRAmTZQwpLmhcI4VprJSF510Nn+OnPfgovkLx09hW+/rUnuXxjifPnL3Hxha+7m3eP+Nr/PI/OM5RJSVOLxKCEQVg9/JwNoVIEgU+WW4rc4gkPKS2JBxKL1AYpQ2I0sR8ijSTNc9IsQ1snwBdHe4t3lWK1e49bryCwHsrrE4kSRjhVVpBgNGMVzb33z/HMq+fZ2uxRqTTA+uSFK9ikdVW83dnU7f6F6PeKdzSR7zpxD6Voh25Nt+0/1tlCWeEqduH0EVxbBYwVu3vYjqmv1Wb3RhVm6Ma9z+fw8sXrRGHE6uoKn/83v8fG+jrdXo/5uTlsVOfI8aOu4gh8SqFPu91heWOVTKdMTE5QCM0gT9DbOcoUDFrbZIWmXm8gpaAaB4TKYPTejEpdmKF0jCDTOYl1IjzK1mi2YlRfEHhgRAcZWiYmy/hjraFejULhjmNaGzLtdn4pJdKTIJx5sEXuqwYZRY6yLhBDNTiFkD4YjaXAIyLJMpJWkyguO/GltCCKQgbNbUQcYn1FlmdUKxXG4pg0T0kGCVhLluf0+/19k4nFHT99P0RGdQaDHJ0XBMJQDAa0222M6JN2M3SRQ+A5bWuctnu71yExXaQIGBufJ9M5ZG0qvmV0ZJzF9RaxqnDi2NvbeAH4oUcl9hkrR8zVR8hMznY/Za0/oBAJYQ5WKLQyIBVSD+9ZLNrRjtFaI5AumUvHIFTCwo4+kOOO77mO7wztngk8x0C2lmef/DzPf+NrjI9NYsujPP7Ep98q9nZ+yhZQ5CAj94zAbiF4p930zVvX0MLNTzRug1bSd/4AvmIkLhN4CmkNCA9dWIR2J1akxOiUk4fm+OC7zxDHHlJJ0k6PmelpgsokX3/hJadT7u0tneANWgjtRK5KfjBUQHRvyCIJ/RBbaIqexkqnSS5EgK8U2AKrM6QVYDW9NMAXToDLopGFBgGeJxkqcb1tCM8ijWs3Cc/iGYWnwLOes0WUwxOYNhybGyOiz71nZvnmMy9yY3GZxsgM5do41giUdm0YI+yu2J/dx8v1e3423/dP/P+I23vgYpiEhBWuIh1WKtLaoeKhHcpTDocBFrRxxxopbqvEhxdgR8GMO9jN/vYv/ffEcYyUkoH2AIUNqyxu9/jC7/4+p++7l7Ac4ymfUlxyfT5pkULQG3TwA8Wh+YOUgwCbZqSFodAWL/QxxtKoVZCihDB7U7B1od1GpiSp1hQmgMTH6hqmGMEKqJTqQE6v32VrJaJcTRBeipCuQsIKtDYUOnebmHTqbUI5/QmLZb9PJAqDobqk+2yFLQi8AOkFFEaTFT6DTp9qVML3PJJ0QLO5RbfbpdfrMTkxgdYFcSVGZpJKrebUFPOMXqfndJ4x+z4gxdDGC89nZGQeayTd3iZWSDr9PoO0hxUaLXwiT+DJgMwKrBZgJKGn6HXbWOvB9jrSE/ieQckIawR51mF8YpyDs3tT0iNP0QhCpqtVjhydJ0lS4tVNTGHJjGYliEEphHR92GKQ4SmJkRlaZG4egMbYwg3erEFi3LUethfdc/D9PLBvHbjzrM+Nq2f5v//l/8mRAzU2VtY49vAnOHb8XqwZussPX9xrbXPl3Kscu+c+qrVRGPZ/v58tJBt0sUogPAVeAIVGCje0TAZdsjRngKGlXcupW1iUkthcowvDWD3ivY88wKGZcQprGHR6pP0+pSDgZncNjKbQhjTdW1RN+RIrDEZrAuPaiAYJQmF0jhV26CVrwSZO4lY6eQmhLF7go7MEqy1RqUzabyEVeIElkhFZloM16H28VH2lMDvgDJXjYZ20h3JaREo5ee4oNDRGSlQrBis9PvCB9/C1P/kGz7/4x8wfPsOxI2eQSIRwMhI7dY7+dzg9vaOJfKc7NGzCYna+Z4Y648Mq21gL0qmT9dot4ihGBeFt6rRiN5Gb3cRthmJV+2sK93JITILWlsboGEjIioKEgkurK3S14cjhwzTqVdrdFnG1TihiVFgj0dBNO5Rjic+AXIIvDYEU5IU7XveygatE9xEk15l2mjOewBiPrB2RtEKiUpnRqTF6WY+BMpSjMhO1CTzhkw4UcaWLsB2EZXiuUWhrnIaDdCcZh5Cwrkrcp/KLQp982GryMJAXCF04BxXpnMaDICTyA3qdDttZRt7r0u10nCZJPcdYqI/UaLVahL6H1pooKDsbMasJfQ/K8Z7rmBxfwPcqKBFz6vgxtlp9kqrBMwn9TkGzvU6a9ZGqx2Ql4sjcYV45ex6sJE8yjhw5xNRkg1dfe4NmUtAfDEhJCU2NS2ur9LI20wem6HT3FhGL/IBRL2asWueeD72X5so65vlXGbQHbBvNgeMPQFQiiEOMNXRabbprt7hy5Qq630cKSaEzTDFAD7ooY5HGpeIdBIzFaenfcVjpjt0649L5Z/mjP/gCWZLw0kvXePjh04zWa2S9LYLyuHu5cYn2/Csv8oV/+X/wyBOPMHHgKNPTR1g4eS9RvLfS4O2hjd5NNGLYLnQDbE2n02J1Y4N2FBL5IcLLyIc9Z9trYfKc4/NHeeDe08RxgNaabqeNkpAPOlw99xpZv4MUoPfRj81Ti1Q+nieQRqGtxI+rzM4vUKt7vPLC005PdWguY6TPwul3MTI+SalS4eDMJG+8+m0unH2dD370U/zpk1+j1dzi6JEFHn74cf7gy18mywt0sXda9IbDTiEsSIOyFiUMvgJtnfWc9GB7a52z5wc8+vBxdK/NaLnKxz/8XqQyXL2xwbUb5zgxdx8YULfltp1NeP8S7LY13fEr/wzCStzRXYARcohAGbZchn3foshQHsPqUvLiC8/znsceww/DoUzWW5ZNFvf7rLW3PSD7v3mDxFqNsQqjU1etJQlWCKznsbm+gSwM1dMnWTi5wJWVdSqqRBTWKFWrFFjopfhGE/kh7e0mra0mrV5CP83p2YIs16SDfVAraY4Q1g30sgrd1RyhQkAx6Cf4QUAgPbqdAZt5EwEcmprGDyRBOEAK40S8pOeq8J2KfCeJW3NHM4PA952CHBZhNBafXLtNsVKu0N5oo4Sk3+vSbjUxWlP1Q8pBRCFzTJKhPJ+oFNNsNlFK0e128YY98t0KdD8xovkzYH2UjHjfu89wZWkLTx2m22yysdZB3IpI0w7Ctjgxf5AHz9zHhSvXMcZDa6hWR/jsD36SWiB57Vqblc0NhAo4c+IkLz3zHHmeMuh3aDX3VmFUShEqjygMmTp2BM8PWH79IqHvQx7wvid+gDCu4vuSIHDqlYPBBt968SUsFt8P2Nhc5+z511i+fA4ygzTanY4ku+iq7+cIbXEn76Wb53nuqd/n/OvPsnD4EJfPJWytWl5+/ttsrK1x5sGPMTlzgDiOWV9Z4aVnn2T5xpu8/nKbG7/7JaZmjvL4h36IH/jMj1GKa3f8/8thD3QHRmqsa59kaZ8by8tEUQk/KCN9D5RgbKyBtG7A+OA9R6lXSvjS2cXdWlqmWquzsniTidE67e0NijwjLO+tflivH0IIyLKMcrnKymabe888zsHDV6EFiwAAIABJREFUx+g0m+SsoBnOjvAojM92/yjp5hhmTbHdGmGrdQ8pVRZXxvDjJ/AGPTZaIzz/2hiZ91EKaZDB3oN5TzjhM1fxWzxr3QB3Z7Yn3Ik4x+NPnnmZMFQ8eHqOVqdNo1Tn0x/9FJeWlnj6my9gKQj8wMGf3eDE5bidi36H8Y4mco1125kQgD909xHDXpZkbXOd//Sv/jX0oMMPfeaH+PRf+AyrS4t84fOX+dBHPkqn1+eeex/ADOE9xhjMbn6wiGFSl2rvnb0ca6dNLEP6aUKAouFFTNRGmJ2dYmpiglqlSlEUFFs9js0eZ6MdIFSVLA8YGTtK0Bjl1We/Qtq8OERWaKQeEAgLRYBM9T62AdBsZQShpCzK3HijjYrLhGFAXmRkW+u7+uq+75FmGf1On/Xr18DzOXZslMPzCZ7KoTCuX84Q0WIsWkNiM7Ry29+e1yXrI4QEawiCEBmXEVkBygcvxOQZcRxTKZcYaTQ4urDA8rVbRFHM2toqy0vLHDhwABv6KKXY3t6m1WpRr9ep1+tsbGwMVRT31t9++cLrDpOuPK6vXKY0OknaLxA6B1UQliS1egOTVLm1LZhtWQ4u3EdiQnwVUIQRxij+3n/7d/gv/rtfozw6ixcL1re3OHD6YaYGBV4QQLi3PvtAGFZEiuhs8+Q//yKDwYCVrRVu0CGt1Tk2PcHx2RnGayFhAMYhHvnrn/jA7gNoJFzZ6PDRD32Aohi4nrSndvugUsg7QvLshBDwxktP8Ttf+oe01pb56Hs+wr/57d9kcmyEqekSD5w5xeWLZ3l569doNre5fv2aa6OoiOMnZpBBxL0nplldW+L65ddI+p+640RujHEL0E790w4hg9rk5DphbdvHCzOCIMH3JGEgsEVCPhjwFz/xId7/4EmkTihVxrh08RKf+40voaXH6SMHWTh5hqTzT1FY/H1kfVulHycnxK9oOkZgZ0JeuRXz0mKEFAsw/SBelFOq5AQlgSdqLF2v4fULAr/NrXYPIw5h6kf5WP33WJy7yDOj7+NG+zHa3RhdmcKKHLVPC9D3nKeClAVaxEjZQeFTkhoReGhfEChFtXyQw7OTPP2tl+j3+tTKHvMLlsBKzsxOcvRHP8Nv/u5X6fYGHJtbQOQQByFFVuD5EXh7D39vj3e2Ih8OYgSucrRDrJsU0mF2/YCjx0/w1Ne+ym98/os8/+wL3HP6BFE55Or1K+RacO/9iiLLcM3HIV5z+NeODrnZpwKdbJRI0wxrPI7ecz9j5SrjpRon549QLft84+lvcPXWEmmW41cbPDh9mKIweF6BKRRJAmEUc/zEvWzdFOgsp8gykqLndJB1+a2+9V6fhzEoG9NvCkxhkENDW8/zh449A8Iw3J0f+CbHA968fAmjD9KojzHaAGsyTDEc/lqNlQwlOrXDr+5zPwSechhyJQnDiKhcJyoMvUFKrg3lUkytUmF6Zoax0VEmJ8Y4f/YCvSShO+hjJGS6IBDu6JznzmA7z937V0q5TVHvnbje88EfQpsCpSS1cp1StYrKJcpK8CRRLAlDJ1+KVVSVoX5ogb6JwYAhIyqNAIKHHn2czY6HUdBPBnS6HXQ3pzD7e3YmwqNZGES7S/X6TdK8YMUUbKgS1ckZTh6dYrwWELKLJdwFkDnGj4OETtdLTE/Pce3SG/ieI58UOGsypNxXbvn26HbXuHL+KZobN5D+Qbb6huNHZljfyOmnIYN2wsRIg+sXXmR17ZpDTIxOc2NxldWtLqWZU9TChCiucnjhBNWR8Tv+v0GgtIeWfTxtODZ7gg989DGur1zjtVfPc/yeBUYao2RJzsbGOp1um15bs3BolorIeP35pzl+7Bjj0wnbmy2uXL3B8uYaMj3F/PxhN+dSEaXRyT1XYaMDGOtRiAKjJcZKwEfgYazAj7fJzAqzM9PoLGB9ZRuhNNg+SX8JoXI8P0CJiCMPJ3zyTEj0tU3+9Vc2KTgIWKzVOw2wtw1lXdqUFjzPIooUaxI0igtnz/PIY/fy1d/7f2hubTI1c4C42uDG1S3KJdjYXOehh+/FZpIgKPPRD7+fp5/+Fq+/8SJKrSKylFAfIKzUaUweAk7f0RV6ZxO5GbZRlJsc7wwmrfDBWMZGxvj5//IXCMoVnvrqH3H27CUGgz4f+vgTnLt4jo99/DNDwDyOQSVAWKdF7LDlQ3ii2fsBedc9x+h1B4w1Zvn5v/OLrN1c4tXnXuTKxUuYYkCW5mxsbTN/eIG/+h//LE0kN//4RQadFD8uU62OMT42wr2PnubCq6OsLC7R3NoiLfogoLfdx1pI0sGe65BIdCJYu9XCkXgEQpvdee0OXl7hDlxaa1YWr5NnCdevLjEzHVGtjVBoZ0zhsOUOn2uFxSgDyg2C94p4SPzxfR/P9/B8n9pIjWph6PYTTJpSq1Uol2K0zlleXmJpfY00TRAI4jh2TNgso1QqobXehRzqYdUZ+D5ZsffGduqeRxHDoaAfBkglCI3CwwfpgzQIaVCewFifIO+jGBCJGlqDIUV7Eit95g/NkSwmpNaiojpBeRxdzp3l2z444RQfzyhICm5sb1Eg2Axr+FOzvO9DH2C6ETt0Fdq1AIXaTco7R2QhBJVA8dh7n+Dq1ctYm7r70vNg54y0j+UcsEsSuXT+LH/ytS8zMdbg+Mn7eP6b32SsWmemHKKFYunWFTqb18mTHnOHZkmzPu12l/X1JeqjB2i3Npicm+L4qXu558yDeGq/8+LtazBAAVZQHxnh5/7GT/HJH/wgW+0tnn/2W5y8/wxxqYzNDEsrK1y5cY1ESx558H4OjlXort9iaXWVxdUNXvn2eS6ee5M8T7ggcq6/eRFhIYrqqNLerRUpBFJkWDr4fhXfU5QjRTJokiRdjh2LWVtv0t3o0m9OMVKvM1JfpbvVopd08ChQuSLwypyVW/zkh6c5t6T50ldu4EVVet3+EOu9dyL3ZYqUBoTl5tIrtNauMVEvs9r0+J3f/zrHj/4cn/tnv8ra+iKjozPE1TF+5mf+CufOX0Rrw+LNa/z4T/4k3UGH8doIH37iXfzbP9zi0qUVzGAL1buE8UKmk5PAx+7oEr3jOPK37NnkkMxkhx+cBGM4cmSBv/kLv0A1jvndL/4mizeX+dof/gmf+MGPc2RuFlNksDvA20G0mLd65ncQQdZHCjg8PsZ4ucboQszJ2cN0mk36vS7l0TrNTpuisIyMTxIImJkoM3fwEHNHTzI+M8Xs9Agl33DvPQtsb2xy8dx53jz3BtevXyXNtnFl2f7YgNZWj6SXoXwQnsH6mixLkVISBMGuC1KaZWRCkGgNwm2Cly9vcuDwNL7vUeQDZ1c6xDBbabDKgCf2dTzxwxAxtNIrlyO8UBCVQybqE6yvbtDd2mSQ5Fy/schgMMAYQ1JkRHGMAOIowpOKje0WY+Pj5FlG0u+h89whDDyPoijw1d63W+xHztYP8L3IYZJ3jXGt2/DlEGJKhPIseXeDJM0RMsKLBS+9epb7j1apkBEq0ARQWAIVkIUZJs+xZu8Wz4DCzU2kwKQa7XnYwOeh0/fw8IP34tp4O8w93NfWFRZSuSG+tg4FdODAARCCvCgwWiE84QaHUqDuAADoeuOCKxdew+gCz4uQtsVYVdLqJAxoMT52gCgukBVBR/h0en02Nlap1escWjjEAw89wksvneXgwXkefvRDnDh133AYemdH90CBJxNyypx+4EHe/fgZfAUxisceOM3UoQNIpSiSnIPjNR44vYBBEYURUegzMzVFYTN6/S5/9OSf0u1sIYE3L1zEakHgOXcgvQ/8MLJtTp2a4r77D3NgZhRfwdR4DY+c1ZUNwlJGsznBhfPXKQhYODpJGM+Rp5anv/48y0s3UBryfpvl1y3Pf7XHrRtrTBw5xMqt69Rr07SaBcU+La+Xv/1bTEyM0u93+ZOvfxGTJUyNTHLxZsHGVoYMQ07c9yDychXfj+h3c555/jcR9MkGEdev3OSHPvUD1Bp1BuQ0qh6PPfIY262QlVuvsrz2PJqEZvcK8Mt3dI3e8US+O3y7nXZvDEjj3GWKgoMHZvjghz7I1QvneOPlb3Pt6iKLVxfJ+138sITlLVec76Tvv/XvvUJ3W6A9ImPwM0sUV0hlSnn2IFmukaWQiSBAoEizApX1+Jmf+jEqcdn5RSoPW2R0s4RSqcL0bJUDs0d49+Mf5MrlC/xv/9N/Q6ezTVbsDT8skqF/rRRoKxHWYoftiCAIUEq9JV1gwSBIcENgP1AEBvqdkPr4HFl+FqFSrJYI4YMnMZGDTIp9LE/8IED5PlIIwtDHi3yCOKZcG6HXTtAaBsmAXq9Hr9climKiSowvFCbL8YbQz6npGQ7MTNHvdui2my5ZWEvgeVitHZ53j/D8IStOCLxhtereP4AduiFZfGXptpqs3zzPeC1ha7mN9EsYoVm/eRbzgw+zdv0SUThPIkKUdENG5Q0tBvXeCbSXpySmIPB8Br5HbgxlC5VanTj0EUM0EKhhMWIQVlEUhmuLi+TWMD93iFAJpqemiEsl+k2DVBLheWA0QirXUrjDuHrxDRr1Gkmief2NVzH9HkVqyHQXWymjRI2NrQ3Onb/K1PQMYTzG/NGjfOzMGZZurTE7f5jT9z/O8dOPIlTM96OXF5PQqIasb2ecXJijVg+5efUSb77yBg89dIaSL7BWkw7aBGFIuTyUCVAS5XsY4YGBamOEA9Pj6GLAdrsLCqQfMlUpcWiqwc1ib+mEIstZvnCRcvYKwQPHuPeh+7FmlemJCifnD1GrlhBWYj9h2O5vUo5L3Ly2TBBGTEVnWF6cZmtjk9b2CkEzYPH6QaxX8OmPHqW5ZKiOHmWrVfDyK5f2XMc3n/08vu+TpgPQHrPTc9RLUzzQGOex2ggzR47y83/3l/j8F3+HG5euMplrOttv4gcp/V5Cc2uNb3zjj/nxH/9JettdvKDMxPgop999mAEbZJ17uHHtBYTduONr9M56dt5O2b0NEmfQw6MqBIHi6pVL3Lx1nR/7K3+ZV88c54+/+nW+8odP8cLzL/Hzf/M/42M/8Kmh07VLajvJe0enZMe78+0iqozSH1hmjh3nl/+XX+ap117m3Q+8i7/1Uz/rimjhoGHSDyAIyLIUf7SBTgaYrO1YbtKdKK6vbPP6hWtcvbXM0sYyi0trHIxHOBpa9D7wsltX+2xttKjVPIJSnVAqlFK7EgB5nhNFEUEQEIYhnqc4fOwo3VaXx8shT5RTuosXaJvjmNETtLpL2LyFUAYjHUbfarvvUbFULrsWjlJYLyT360yPzXNraZtbS5u0uj0wGt/3mZqaplKpoIWl3+uT5k664MyReR553we4eP4sSZLgeR5KeZTLjkC0tblJvs912Tk5KCV3pRp2PmcwhKaPNTlrN6+zdvkiVdvhwptXqNQbXL52nZXVTU68+2P8g1//JuMzs/i+wgt9hLBDkoar8PeruNI0hSCgawUUgvm5I/ylz/44p48fx7MCgaSb5Dz3rdeQAh558Axf+NJvcerkcaYnp1i8cpVqWGb2wAQHDx7g+KmTXL/SJazWMV6JJEncPZrt3eK5PQ5Oj5HXFcIr0ep0uHnpZcZLBQ1dZuv6Bb69uUxQnuaBd70fayM2tjbRtsKLL7xKQpm//l//I+JgdNcQ/PtBKv/we45z/o1LPPHEPB8/OYLf8Xnx6afo9zfZbh5B3VwjzTJ67W0Ozc9RGhlBBRFFltFtblCOIwLls7m6SSlL+Oy7TqFsytdfvMAgzXnvPUewgxWm6pU919FTkA5iXnmjxwvf/i1mvvEypx46w9ShSSZHIg42qpQiHwFEYUy+3CJWHtVazMc+/CgQoIuMajWk6Hcwus8Hpg7w0suv85sv/Rrp9kuEpYhf+NmP77mOw9NnGGnM8yM/+pcZnZhGFzlJklCrVTG6oG0LdBAwNXeAi5evYa3lUGOGsfF1zp9dZ36mwbkr5/nH/+KL/Cc/8WNcu3SJJLeEvRanDh5goj/HWL5KFN75MPwdrsjtbqKVnvqOynknsSspGa1Xec97HuH5555j7MAYf+vv/lf81hd/j+eefpLPfe7XeeDhdzE6ddDBfHaP3iDMW8zRvWKj2QJZ4luvvMzmVpvZQ7M0Wy3ePHeOmalpJiYnhnR1gS4K8kHCyq1bgGV8dBRT5OjCMEhT/tH/9c94/cI14tooXiAJohKPP/puzn/zD2ht763p0e+n+L4HQ+aq1gW2ULuaNGmaDlUhBZ4v8ZXHeDRKFPjoQQcvg0bSI7t1Fs+7j/HGAq3BVbK8A8K6IzzfuYF+r/A87y3jaiEpVxrMHphndmqBwPcQWZssGWCtHSZohTKG/hDNMDE1SbVeR0pJq9VGa43ne5RLFarVKr1eD8/3UPtQ9G+HSkph37onMCgykuYK7dYGV6+co2QKtrubtFoDeqkmLpXJsg3CygQL93+AzBo0BcZoZ4JtvaFCpvmOIuJ7hfI8kqLAGEEQxxw7forZAwfxpXL3nIA8L7h2Y5GjR+bRmeGf/Ma/5kPvez//wY/+KHOHD9Pu9smyjErZx5gM5Umsp/BKMbEvybKUzN75g/roE5+m3+8wfeAAX/j1f8rq8hYm6qOCiLljp6nVZlhaXuPKtTcR4SjtXk59q4HF8ND7308UVtHpABkpLEPy3B1m8/OXr5PkAy7cWOHh9Tb36QGvnz3H6uYGR048yuFTR5FFiW6nTZrk5IOcPLdkWUar1UVJRVgJ6PW6nLt0g5XthPFKCaFCx7soEpS25Pvsa1YX5LlE2DKFqSMKj8iLKJXryEBRCEG7nzAx1qDIc0rVEuPVEYdoMhF5VtDpZCyvtSjFgrHGGGkiePeD7+Z9v/IAuU7oD/oE1b3RPD/9kz+HsYJvPfsMjWqVv/DZHwahEMKiTU6y3aWztkRU9NCtNbJBQm1ulIo/DkWTe+49QW9rnW8uPs8nn/gwv/u5f8Hi8i3m7nuQvjWcmC1xz4FHHE79DuOdZXZKsHo46LFvMZnEsNdYFAWvvfwtZkcb6CRhdGQE1VN87BMf58jCMX7tV0OytE+pWnE8N6cYNfztb+lJ7JfIV9a3CGPN5quvYuMKiScoBWXOX76IMYZeMqBUKlGtVIjjEqUo5B/8yq/wrTdf42Mf/gg/8dkfoSx9tIY3Xj9HbjyqNYXKLbMHJ7j39CmuvvBVqtHePT9jNEJaJL7bzf0QOzRT3sHr5nnOUAKFKIpQUlAfjYCQV5ZvMVL0KQ9ySou3sOIQRPN0g2W6dsMN16TdUfZ429hJsG5IqSjHZSpBxKEDcxyYGePfZk3yQcIgTfCUR5qmbK6tE/oB4xMTHDl2FG0NSZLQbrcpigIlFeNjY5TLZbIsQym3AewVeniiMsbge/6uto4xGWlrlc1rb9LvNVm7dZWJUkyv08KPRwlKIf10wMHDpylFFYQGXaRoUrQdMneNM+E1xuxbkWfWYKWisMbJCIcBKvAJAh+pHAmtEoW86/RJpiYnCIOQLK6wvNVGIJgaGWHx+g2yJGVitMZIvcy16zlxFGCiAEROHLoe/36xcy8vnHgYFAy6TZABlWoNYQumDhykNjrGc898i+Z2k/njh6lPTnD/9CS3rl6kn3hMNqa5+foLFFpz8Ph9xNWJ72by7xmv3thkZqzMjcubPLTa5wnlsbg14NnnXuOxRy/y8CP3EUdVBt0OW9YQCBCewgL9Touk1yUzBdsbW6w1m7xw8To6S52shIGLi+tMlUe5trGy5zpUUaC1xVJgxATLNzaIglcwekDp5GHGDx2hHIWEvqK9vUrabWLCgL7poeOYWq1KuT4CNOi1u2xsDmBjm3IsaIxUGR8dpVyrk++zv379K18ltzmvvv4GSvrMHz6Gkc6cWyjB8uYmv/6r/5CqMoyaNtrTvPLCElJo4lKFp596nY3mJlk8wTPvfY4wCqiVfOg3sUWGHy5gdIVsv4XcFu8wRd/1g4fd7V20yc4t1e8P+Ny/+hxZs8mBQ4f4zF/6YY6dPEVRFMwfPsTP/uc/R6fdolypIFGYId7zLcnyIUZ3n8pP+iW6SUqSG3wE9clJ1lbWefKpP2UwSDhx8iSlOKbT7RD57iF+6qk/ZeTIQT7/pS+ycPAQH3zXY1g8HnrwYUYnZhDKxxeCg9OTvP7aKxTGEod7MxmldCJKcSkGHP5d+r6rIKXAVwZTaExunOpi0aMUG6IYsrGYWxMxXQLK2wmVpUW4XhCOTcPYDHlYom/fRBQ+6v9t78x+JLvu+/45y11q7+7qbXqZ6dm4DsVF3CQREg3Fcux4hRPHiJ0FeQhgIIj9mvwJAYLECPIQOHFiSwYiW5YtI44ty5KijRRFWZQoitsMOZylZ3qZXqqrq27de8+Sh1M9MyTMbtIBaA9wPw8DdAPTfbvq1O+c81u+X394cU9pjRuncQoEwlnMcIAfDVk6NkMtTVAIBtmQuBbTbDaJpSJKExaOL7G8ssL07Byt9hRf+KM/xFpLEiniOA4neCnRWhMnh4tV4bkpzhRanAAcrhyxvbbKXm+P3d0tBvsDWnHCyGtkXGO/tAxHhtOn72G4u0m2u4aopUgsTgQxpbLIsa7AWod1R6RWrEPGEVIpsjzn9Qvn6T2+T7fZxDkYCUik5IHlE4hYk8WClaW76bQnyEeONEk5e2KFKFI0mxErp8/y/Ve+i05SvFJYpUAG9cT3jIjx1pDUOpz70IfZuPwy0Uggo5TXzl/gxu4Gk5Ndrq3toustbN7nkXMneeGla1x65bskYsTkwllMcRLPNE6I9/zhL73AiAjrMrZ399gvPPtZzvrmVb76la/w6BMfZnb+GOuX3+S6tbjiLJNTU2itEfmQre1tVFKnriI+8pHH6JucL/75lzDOAILcCEbGs73TO/Q5tLGhMwuPVJOYQnPtUp+y+AE7a2+hyj3uuecsaXeSU8sn0FJhHfT7u2xurnLtWkG7UafbnWJmbhExF1MWOTu7W7x+dZtRrpnudGhPpIc+x/Wrr9PoNDixOMNfvfwWn/3jP4GoRpTUQWlSOeLK6y+RChAiplarEddrCJ/SH/ZxzpHtlxT96/zlX/45M52Iqe4UszPzeCzD7RInJYX9O9tHfkvJ8G3a414ghKbVmOSjT/0Yv/PffosfvPIaJ8+c5pd+9Vc4aBRfXFzEzx+7+bWAm10OEDobjkojAPSynNxY4rTG/nDIiU6Hwd6Q7734IleuXuWRRx7h+PHjnFhcYnZqkrTZoDszycLKcaSUfOs73+bUwjKdTpePP/UUFkG/P+DqxYucf+U6TbvK5bUbzDQPz/mZsqTdapAmcejK8GODC6FQOqLMM4QXWOvRGFqtkokZj6qDT0doLTBCMmrX2YuH1LY2aG4MaA5mqS1O8lKtjXZDlDx8wjSOFDiLkkHHfGftMm9Zx/J8l2LguXJ1lZWTpzne7vD6+dcZDoacXFxkbnGOu+6/l5Nn76bT6bKxdp3hYA8lPZGWFHlGLU1Db3qckOdHac+YMBAkVeiHd2HwJNvfYzjYo96ZYj8vqDcGyFobSsFeNiCOFb1eH4Eh1rCxfomZlXtvFdWx4Eq8NyFffkQ3Uek80jlQirIsuXptlZdefpmGjmmnk0GVUXhcIpDjPfKXf+4nmZ/vMndshq04Z1IJ3P4ImSYsHT9F0miNbxoKbxOcs+j64evjbQgBhLTbA488ymD3Ml/708+Qbe/RnZklrtU4Nr/EK+ev0Ovvc23dcWzmFJ1uyv7eJVSnBUqMpZqPvqXdzrG6JDF9ZhOLHGww2LiE663RBp7/9rdp1JqsXnyT//PHX+Dy1as8+OGH+MgTT4TWSec5c/Y05x54kK9/7Zvcfeo41D7JxvYu3/vWN5HCo5XGlJZW+/BBLW/CgJv3jkhqhJxgmAk2rm3RqA25sb7G83vXERjmOzPMTs9x77lzTM9MMTszQW93m43rq7z4whssLF1murvA5MQsczPTtCbb7FzfZmdrl8WlOU7NTbzrc3z8E08TJZq33rrC8z+6xJe/9CXO3nMfDz78MDu9Xb7/wrPcc9fdtOsdBoMReZ4zMdOlLOHqlQuU5RAVJ8hRwTe//lWe/sTHOHF8EeuDPrk3FofFH3HguJ0PXDTr9jzorQ6T4KYRxym/+Iu/xOLSMr/5H/49n/1fn+VX/sU/xxC6L4JIvUBKHRr35a2feztHda2UKESkGZQF2ahgsL+P94Tr316Pr33zG9TSlLnZGZaOHePY0jFkLNnv71Ov11ldu87OsM9+XnJlfTs4BmUjtjfXmZ5scfHiNUpVZ71/eBU+0gotBbYsg7KcKRFCoSONVCFvPsyG6KhkcbZNd0bjdYaIVJA68GHCzCiJmUvZ6TgG63s0tvdILk3TueskQ3cRKw8/6QjvSCKNx1OTgrzI2Ny4yPnzbfYHGUvLSzz25BN0p6Zodtp87g8/h8sHdI91aHbqOARbO33On3+dMh8Sa0mkJWVZMBwMaTbbMDN3ZLtbmOqUGGtR4yDqga3tG/T2d1lYWGB6bgalQxomqTtubFyl251kojvN9c0b1Fsddq9epHviHNZ4rAiFaaU0Ii9Ca2R+uAaOVwrDeEhLxxhvuXTlMmeWVji1NEm+leEbmqiZ4JxDF56Pf+xDJM7RGJXYQU7x+lXKRoPhTJdGo0ajXidzFq0ULorxCJR+H24+IqQUjDXkxtKZnieqNXFeBGmE/hZrG+v81D/4JH/10gVOnznLSJSMzAar1zZ57JO/Sru7jIrb4GXQJHmPsfyhe05y6fIbrJyeZ76dcOWN12irkifuXeHVyzdYXjrO1770Rb7x9WcYlCUvX7zEl7/8tdCBK+DXf/3f8OTHP8Uzzz7L1rULLD/wAI8/dD/zYg+B4/rmHtmNPo308HBkbIkXQWbZmRIpYoxNyXsNXvrhLifPah559CyNBgx3Nrl46U0uX75As9Vkdm6OxWOznDixyEQ74Yt/+nt0p5aYnz3B6bvuY3pxgWPHZsgv5GOoAAAUnElEQVQGOYMsA949kK9v9rGu4PKVVYa9bVzWZ/PS63x76ypCwWs//CHLS8sM05w4jsDDmTPnePPimzgncBbiNKWfFUQCOs0mZpSz7wxJqpE6GtfG3vPq+IBH9K29ac920znEhxOSpyQvwwfs0Ucf5Hc+82leffk1vNDgDJ6DlInGO3BuNBYdkjd/ZggE6sgcuRMaiw2OP6bkO99+Dik03ekprDFBSTHWXF5f5eU3XsEJwcm7zpBmGZvrm1y+dIV/+mv/inpa5/j8AqNsyHDQpzPVJJKCVNUYZRmxOvwEemx2GmcN9XqKlQobKYh0EKgTDh1rJtoRJ852SNIM6wtqSZ0okaHn3AdxK+9hJAxF4hkuxmzPWUy2zs63V2m1Z5g8efgUnzUGpRRaKUajEd5a6u06q9cuoVXEUneCvWuvMrqRMNuw/Nwnn+DG+kX2t67wwreeYXL6Gi+9/AbPf/dZbJGTaM3+3oj9oSWtWyZmZ6hpaJojZEp1xGhcVDVlOU7LKLyUFNLzlWe+QZkXTHemqNdqdCa67L35GllhWFo6SdJdYH/YR8U1+lmO8EH3JBsV4TqbGZwD7w5f9oVxwfAj0tgyx/V2WF1bY2c/Y3ff0LSK4TdeRL15kWx6gssrUzz36ovI9T3eGuzyynTEv3zsaf7xg2f57f/+2/ze5z6NlyXdhSWSVpN+VpKXlvKI9tTb8d4jXcTnP/1f+fKff55E5xybjpnuzjE51aWVtrnrnkU+98fP0ukq1jfW+Ps//2v83D97gCAoFzbRv4ks+V88/yNmpzu88oO36N7/NFef/QFf/u7LGFNw6swZfv/3v8B/+s3/wm5psCLGGc1bN/pEhGG9zaHitz77R/zn3/0fRKOC+6/s8tGnHmV77TrSjbBlwu5gSBIdfiJX2mGFxzmLdE2szJE1Rb11grnlB2icOMczr11DSkcrrXNs5hhPPXgXM5NNEu3RwjPc7zHKIlZWnmRvb5v1rU1e+IPfYdjf5cHH/h5PffzHmZmZOfQ5jDEgPCsrJ1hZPjb+niUvC4ajER/98APoKKI0Bc55Njc3+cKffCZos5QFUxMdamlEp1NncXGRKDIkqafeSIPg2jircJSkxe184KmVgza3g6/FWIxHcNtJ2odT1AMPPRh8JMd5dOdcGJ8V/qYN3O2pFCEOxtQPP5FLk1GORnTiiGGkgztIlBBHMXuFROoQ0GKpmGh1yU1OORrRbtQR010unn8Dayx7e31e6r+KlBItJbm3JLFmZrqOTBJSeXhO2BqI4hC8knqd0hYhrSI1Oo6I4pR6N8LJIKSvJIjUo+VYOc9LrJMU406MyEgKL7HO45SgfTZh98o19PbhE3PGlCGQa02j2UBrhZIR9XqKVjGj/R0GvQ2iSFMUJdJ74kQwNTlFtzvLs995nhdefBnrS6baLSZaDSItGZWWUVEgpWE02mOwf3hf7MFEqNaasgwtXWVpKK1DJ02On7wLVxpqUlEUOXFco93uMHdske7sEiun70F7QeEUvqbwpUNKTRyHlkMRW8pRRnGU1HFuUEmMxSNkiTWe7b1dtvb32ejtE01PMPnY/dTvW+CZixf4jy89x92vXedn3rCceXyZ+Eyb2bsWGXrHRCMGN0CkEwjdCO2y3uDLUbDrez8IweLScVqtNt2piM211+n1etjXCmq1Bt6vk8g+H3/0cTJbY+vqG5w+fTcOiRTqfSRT3s6pxSkm2y0mOnWW5xf4v1/+CnOdOhPdGWYWZhj0e2RFQSyDvIsxhtKWgCdNEqSEH73wAuVgQN9Dc2qWRnuKte0BtcjRarUopaC3sXP4g9iQU2dcJLbCkjQ0C2dnOffwMmfuXaS5psiGA+amOjhj+Mz//goznSZ3n1jk7rMrdKdmWJyZ4/5HHsFkA3Y21tm+sc5eb4tvPfs9hoOcc/c/yPzHHn3XxwjaMy6sVxE0nnQSEacxzcZBft2jZQ2lJAtTHWZnJxhmA2IV0e1MBgkNPK1Wi7QWWouN8ZjS4VyIb8lRNaXb+MAHgm4f5Ln1vYNe8KC4d3AFd86gdPAFPMh3IoJHZOhmuGX3ZsfXcSeCafJh1JKIiXoNgSC1wcyi02oxGo5IIk1W5JT5CBWleGfRgCgL0lizLyDLgkytc54oDt6ORVmS5QVSKnb39mgkCeURL6+15fjP8YxGGS6WgEFqj5QKoSVRIjHOIqzFChtcaJxCuAOVQ7A4jA0LwFkJBoRVRA04dtckqTy86NrtdvE+aKKktYQ41uOWPShNMVZok0gliOMwet+ZmOXchx5jaeU+Gt1FiDWbm9dwxZDJqSbtZgM3NggRckSkRkxPHv4cB3WTg5vVQVvk7NwCUxMt8iJHC4EsRgwG+1hjWT5+kmFeMMxLLq+uUVM1Gq0pVC2MxHsbLNuccDgt6Y2yI/PDxhmkc5Q4tA6v71Zvm8FoyGCYYco6ZiIiWTrG3Qst7vN9OstzpD8+xSceuo+HmxGTaRufe5oT0/g4xeoUo1PK0lJ48FEE7z0FykEe5KEnn+La+hV2Ni+Sl/tsrV8llY6771nBiQH1eMgrLz6HcW2SqxnTiydYWD5JrXb4Zn4Y951YJNERPtZc+N4LrF94hXtXupw8OUteCO45s8iPfeIJbrz5OvPTHbIsY3sYTEWazRat2PCd73+XpXrC9MlTPP7wvfQ2VpmZaJBowc5Oj1qkqNcOD1zO5vixgYeqSxrtFounFzh77hTHFyeI8XRqKQmWyWbMRGeaNIZWWmOq02EwspiNXUbZHkYUnF5YZnb+NPOLZ3AYzpx7ks3NG9SSw4udSgVZBu9Bx8EBS0lFpCRqrJeulCI6kG1wnsmpE3gctrDYAw8FJUOWoszJjaQows1JRRG1Wvp390QOb/+w3s5BeuRgwR58mA+0Dd9pzPxOu7d3/o7DmJjqkg+GeGsps5xslOGEJBuN2BsMWVhcxHvP9u4uiNDXPtus8+L3/oqNrV3qtQRnPXlRoiKJVppGI0UBcRyxs7NDFscMoyNMXOPgTpKmKaUHoSP8WCUwpJxscBkqgqY3WHJTIgxjYXsXtKKdx5WE1iznCIr7HmscMpKIw9clrVb71mSsDLcmPz7lG+Oo15JxMRbSuI6QI2QySXduhdbUHA8/Ps3c8Tmee+brvPCdZ0hrEk9BoiO0VOhIYXPLyB5+Es6yUFMwxtBqTZCmCdZYlBJEE93guiM8o/4uNtpDCMHxzgxeQJyk7GcZkY+o1Wo4oHQOFatgnawECZJWPeWIfR6nPEIFjfzSBeGx/qjPTm+LJE6D/nQqyCiZqaf86088TaxiTCxIhGZpBFlmGRaSVy9dp2eh056kVBFZnoFQyCTBmcONFG7nYE3XmpP8xE//Q1747jepNRokaYutaxfZ6u/jGdGammdtfZXZ2WmSSDDs79DvdbixfoNao0N3eubIz8c7ma4nREpjteDFC+ep+ZwTsw1m647t4ZC97VX+3b/9Df7s9z6NGG6iZUnfhZtVvdlldiJm8/IFHjmzyId+7CM40+e5r/4FZ5fnqNUjbtzoMXAae9SdwWVBEVRAp1tnarbDqVOLzEw1qUWeqVbKdKdONhyifEEz1jx231nSKKYepyhhkd5iWm3Wsj5bA0t/uEWkNHESkWUS1ZxjevbwVGQ9UeNsgiZKxE0RwGDKEtp5lZTI8aFTSxU6w6zFaEOapHghGYxGwS/AuNBNZQkXjlQQpzFHSCS9jb+VEf2/7nu3B/jbc96H1ddv3xCEEDf7kI9KrWzc2AJjmWx1aKSKyakuQ1Ows7WFkpJeLxQHa40aQgpMkRHbNt6U1JIYLTV5bhBCEKXBnLler6OEIBsOQ9tdHEN8xIncWeI4HrsLJeSEbcxaC8YQpeBMFkb3bbAMy0sHymK8IHTpCayVOONw4ylO4QhqgNbjIk9xhFORH8vXCiEY5dn4dqOJ45g8L6k3mqG4fNCnrxRGavr7PVrZHjqJmO12uPvUIr21YzfH2KUVSATWFzTiOEjjHkKe5+OWRUWz0ca68e1LavLSUa+3sNYQNSQzrS4IQTEyRJEmiiXWFSRC4YxjaAr6ZoS1JcYasmyEiINxhjOHd60U5QjlQ26eyCLihGy0z+tvvsYTD32YU7OTDI0HJWlLyUKtgSmgHMLW/ojcWRIdM8o8r1y+img0sVJjhiOsL8fG1IZydHg30TveJYLevqTZmeNjT/8MjWabVmuKHxqJbiQMsx1O3fMkU7PraKVpzSwwGPS48PqPwMVMzS4x1Z1+34F8spPijMMoj8l3uPfMAoszbeo1R9GQXDz/KqfvOs1P/sIv8q0v/E/saEArTZE4Op0UpSGOHWfPzPPmyz9gfaNHZIZMt2dxwrM4P0HfSEbF4Tusd4MgDuclSVMQ1Rzdbo0TS9PUah4hFdY5rPM0Gm3q9QZTrWBII5UgVgnSOZSKmWu0EOObvLOWYe7woo4xJaubfc5OvvtQUL1evxmzpLK39KPGmQI7Nt1w2PGtWSCcC2YiYw9bKyRSRZRju0chJRLGZvBhfiRuvHdhsw94IEjirR2frMTNCUa4/cQtblbTnXNorUMb07sE59tbGt8rO3sDMI7RyKCkxg73KWyJUhFpHE7DaZrSmugwGA6oa4Xxnnoah/H9umYwGFGaCBEpvHMU2T7OCRqNBo1Gg8FwSH7E0c9ZwX4ucEIQCY9OIzwa58GXBkmGtCOUE3hjKZ1hqASlV1CAkB4tBN6AyUOeRUlBED0AayRkHnuEnG693gj6JjqkIbQOQ0lKKcT4dAHhpHzgqVnYjEvnn2P14vdxNmyo/d4m043oViFbCrSOGAwk9do0aXT4c7TbHaQUGGNCqsm5m2ultIa9vT3i8WBOWZY4Y4J+ugzvf5Ik+NyM8+yKdquJsx5jJK1ayn7eRzfqUD/8qKPyApeFDimhLDaJ0fU2z7/wHHkBw5/4R8wudJnt1Og2UjJV8JUv/gV/9q3nefLDT/HkAw/TaMGF1Sv8aPUynbkZVhZXOLt4AuEKVldX6U512d07vJvo7Yi3/StVwiNPfIpHnvgUP/vLv8E7XQics6ytrVEUBUtLy+Fz9DfMktfbaUgHKMlP/+zjYAXOFqA83WNtBuU+f/b5P+Dhj3ySZGIaM8iIkxhpLcVoD1D8/D/5BUa9NznTSblnuYvSBu8NhbUUQ0vDCYw4PHApVSKSBJE2eORjD3Hm7AkadY2TObtDy/WtPfK8oNfbozPZ5MzKMoV1xJFAiDB8pESQy67LMImpVVjfKg3SEM4l1FqHpzSKori5xnX0ds0oefs0pgxCajiPOEj9Ok9ZjnAIjHc4DzqKg9id82gZUW8k1JLkyAnk2/nAi50HAfxm4PZvX163ZVdulthvmcaOHafHgf2d6RUh5JHDQACtdhstFdJ5hnlJnucM9jO0VpTj3x1FEYP9Ifv7GUMcOxh0HNFotkjjhFgn7O33SRs1rAm78iArbuqkIELR8TCEBKFjdJKi4hihIpSKMMaOX58c6d1Y+W/sTC8lUkisDP6F41R2qCu4sTTw2O6tNBbrgg/nYRxsmEIIkiS5WYwObXvq1utPOC0YY8F6jC3wJrg2OWvAexqNsCkYYygLCz5sCNJ77BE6z967m2kPIUS4rRgzVgpUIVA7R6/Xu7mOnFdICVJ5ImKUktjCUJYGHUVoJREu1F8knkYt4ahRGCWDaXVpLLEMpt5mMATtee3V77Pz0Z9ieNFwWRl07Ll64xpf+NzvUriUTz39KbpTDWINx2cnqIk6ezsFG7KHHVxEC1hfX2Onl/MexQeP5O31JjEejlPMzy+yt9fDGofWf9NSJ6E1dmx0nGUjIh2N17YC44lUTi1SfOeZrzKpB7SSoFEjhWfQ3+XVl35AaTIasaDVCoqZQoHzEmUtMQ4vBPkRkgVOgJOKVmeaqZlZVJTgJHgsFiiMxTpJWmvhvGJrd4ByoKXAmHJcxFfECTSTBCUdtUijtcB6iysMZVFSsxY6756PPEj7RpEGyvDaiLGipQyZAWMtpixD9xOCJE0Y62WO45hESoX3BjM+rMRpSlJv4IpRqH29D1G1Dza1cnsAHjvew3i057bAfOAxKYXAWI8UEukJ/0eKm9rcB2mY8KNvD+qHP0ZeFBQONIJenuPyElMapPP4RDPKc8qypNFoI0WEkNDPc+p4asZQioJ8VOKdIxsEc2FjLVHSCEqG1t4ywT0EU1pUohE6xcsIr/TNjUprjVTj6UYvblMFVGgZjDiCvpdHoFCxH5sQ27FsQfhwGO+gOGIApjQ458eiUmpcNRfBRPigRVQeFJ3H07NIjDWUNr/ZVnr7+yClJE403nlqvkae5+NUybuTZRnOWqI4RqsyaM+4W45P1hrc+H0XQuCsxdk8TMTaYMRhs5xYRWzsbgfRsShsBkkcs7m7jvcSZ44IataCl2ETEBblzbjOLvH5Hr296zTqDTa213jl/I+4vHqRge3TThN2+uu4uiNtxixPzKCEw4xKNtfX2L5+lTIfIYVka3MTV7yf1Mq7885USfgyDM11Op33dVv960gjhZYeUxqINFortJbhBCkEJBpRWjZ3VknmJ9GqBkIjhaQzodjo77C1tkbn+CQqEmgV3Sz0SWPxTRNkf496XzDUawlLi/M00xoaSHWE0grrFL5wDG1BkRf0e4be9R0GMx1KUyK0JE1TnHOkScLiwhSNWkJWlkRSEKtwei6NQ+wXwLsH8qRWQ0tBrDWgg5kMwNhV5OCg6o3F5CE9aMoymK0jEUKjRHBMC8Ke4eZaS1NG+YhaWse6EO7eK+L/902uqKioqPjb5X3URSsqKioq/i5SBfKKioqKO5wqkFdUVFTc4VSBvKKiouIOpwrkFRUVFXc4VSCvqKiouMOpAnlFRUXFHU4VyCsqKirucKpAXlFRUXGHUwXyioqKijucKpBXVFRU3OFUgbyioqLiDqcK5BUVFRV3OFUgr6ioqLjDqQJ5RUVFxR1OFcgrKioq7nCqQF5RUVFxh1MF8oqKioo7nCqQV1RUVNzhVIG8oqKi4g6nCuQVFRUVdzhVIK+oqKi4w6kCeUVFRcUdzv8D/4cNoXWSK2cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化一些样例数据\n",
    "# 我们展示了每一类训练数据图像的几个例子。\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对数据进行采样，提高代码的执行效率。\n",
    "num_training = 5000\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# 将图像数据重新整理成行\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print (X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from DSVC.classifiers import KNearestNeighbor\n",
    "\n",
    "# 创建一个KNN分类器的实例\n",
    "# 要注意，在训练KNN分类器的时候实际上是一个空操作\n",
    "# 分类器只是简单的保存下了训练数据而没有做任何的处理 \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们现在想用kNN分类器对测试数据进行分类。 回想一下，我们可以将这个过程分为两个步骤：\n",
    "\n",
    "1. 首先，我们必须计算所有测试图像和所有训练图像之间的『距离』 \n",
    "2. 计算出距离后，对于每个测试图像，我们需要找到『距离』最近的k个例子，并为它们投票来选择。\n",
    "\n",
    "让我们开始计算所有的训练图像跟测试图像之间的『距离』矩阵。 \n",
    "\n",
    "首先, 打开 `DSVC/classifiers/k_nearest_neighbor.py` 去实现里面所需的函数 `compute_distances_two_loops` 使用双重循环（效率不是很高，后面就发现为啥。）来遍历所有的训练样本跟测试样本数据对，去计算他们之间的『距离』"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# 打开 DSVC/classifiers/k_nearest_neighbor.py 实现\n",
    "# compute_distances_two_loops 这个函数.（其实是补全）\n",
    "\n",
    "# 测试上面实现的compute_distances_two_loops函数 (k-NN的two_loop版本)\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print (dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "#现在需要先实现 predict_labels 函数（其实也是补全），运行下面的代码\n",
    "# （还是在上面路径的 k_nearest_neighbor.py里）\n",
    "#  K=1，其实就是一个最近邻算法。\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# 输出预测结果（正确的比例）\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print ('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你的输出应该有 `27%`的准确率 . 让我们把 `k`改的更大试试, `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 142 / 500 correct => accuracy: 0.284000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print ('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`k=5`的准确率应该略微的比`k=1`的时候要高一点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# 现在可以通过使用部分矢量化来加快距离矩阵计算，只使用一重循环。 (也就是K-NN的one-loop版本)\n",
    "# 实现函数compute_distances_one_loop并运行下面的代码.\n",
    "# （还是在DSVC/classifiers/k_nearest_neighbor.py里实现这个函数）\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# （这里太长了，我没翻译） \n",
    "# 大概意思就是验证下你的one_loop的版本是否正确，会通过一个Frobenius范数来计算误差（这里的代码都是写好的，来验证你的函数实现的是否正确）\n",
    "# 一次one_loop 版本跟 two_loop 版本的结果（这里假设你的two_loop的结果是正确的）\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print ('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "  print ('Good! The distance matrices are the same') # one_loop跟two_loop两个版本的程序没有误差的话，会输出这里\n",
    "else:\n",
    "  print ('Uh-oh! The distance matrices are different') # 输出这里表示你的one_loop的版本是错误的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# 现在通过使用全部矢量化的操作来加快距离矩阵计算，不使用任何的循环。\n",
    "# 实现完程序后来运行下面的代码。\n",
    "# （我给点提示:完全平方公式）\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# 跟上面一样，检测你的no_loop的版本是不是正确的，同样还是假设two_loop的版本是正确的。\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print ('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "  print ('Good! The distance matrices are the same') # no_loop跟two_loop两个版本的程序没有误差的话，会输出这里\n",
    "else:\n",
    "  print ('Uh-oh! The distance matrices are different')# 输出这里表示你的no_loop的版本是错误的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 85.950696 seconds\n",
      "One loop version took 254.330543 seconds\n",
      "No loop version took 2.017514 seconds\n"
     ]
    }
   ],
   "source": [
    "# 比较一下三种函数的实现的时间效率\n",
    "def time_function(f, *args):\n",
    "  \"\"\"\n",
    "  Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "  \"\"\"\n",
    "  import time\n",
    "  tic = time.time()\n",
    "  f(*args)\n",
    "  toc = time.time()\n",
    "  return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print ('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print ('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print ('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# 你应该会发现no_loop的版本是最快的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation (交叉验证)\n",
    "\n",
    "（这部分的英文部分就不翻译了，尝试翻译后的发现不如英文直接看来更准确。如果是看过交叉验证的相关资料的话，这里就没有难度了。）\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation.\n",
    "\n",
    "There are three kinds of validation methods([introduction to these methods](http://www.cnblogs.com/zhaohongtian/p/6802327.html)). The method below is S-Folder Cross Validation. If it's difficult for you, use the simple cross-validation alternatively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validate the k is  1\n",
      "validate the k is  3\n",
      "validate the k is  5\n",
      "validate the k is  8\n",
      "validate the k is  10\n",
      "validate the k is  12\n",
      "validate the k is  15\n",
      "validate the k is  20\n",
      "validate the k is  50\n",
      "validate the k is  100\n",
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.241000\n",
      "k = 3, accuracy = 0.249000\n",
      "k = 3, accuracy = 0.243000\n",
      "k = 3, accuracy = 0.273000\n",
      "k = 3, accuracy = 0.264000\n",
      "k = 5, accuracy = 0.258000\n",
      "k = 5, accuracy = 0.273000\n",
      "k = 5, accuracy = 0.281000\n",
      "k = 5, accuracy = 0.290000\n",
      "k = 5, accuracy = 0.272000\n",
      "k = 8, accuracy = 0.263000\n",
      "k = 8, accuracy = 0.288000\n",
      "k = 8, accuracy = 0.278000\n",
      "k = 8, accuracy = 0.285000\n",
      "k = 8, accuracy = 0.277000\n",
      "k = 10, accuracy = 0.265000\n",
      "k = 10, accuracy = 0.296000\n",
      "k = 10, accuracy = 0.278000\n",
      "k = 10, accuracy = 0.284000\n",
      "k = 10, accuracy = 0.286000\n",
      "k = 12, accuracy = 0.260000\n",
      "k = 12, accuracy = 0.294000\n",
      "k = 12, accuracy = 0.281000\n",
      "k = 12, accuracy = 0.282000\n",
      "k = 12, accuracy = 0.281000\n",
      "k = 15, accuracy = 0.255000\n",
      "k = 15, accuracy = 0.290000\n",
      "k = 15, accuracy = 0.281000\n",
      "k = 15, accuracy = 0.281000\n",
      "k = 15, accuracy = 0.276000\n",
      "k = 20, accuracy = 0.270000\n",
      "k = 20, accuracy = 0.281000\n",
      "k = 20, accuracy = 0.280000\n",
      "k = 20, accuracy = 0.282000\n",
      "k = 20, accuracy = 0.284000\n",
      "k = 50, accuracy = 0.271000\n",
      "k = 50, accuracy = 0.288000\n",
      "k = 50, accuracy = 0.278000\n",
      "k = 50, accuracy = 0.269000\n",
      "k = 50, accuracy = 0.266000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.270000\n",
      "k = 100, accuracy = 0.263000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.263000\n"
     ]
    }
   ],
   "source": [
    "# pass 部分是需要你去补上相应的代码的，代码的要求都在pass上面的ToDo:里写清楚了。\n",
    "# pass 是python里的占位语句，也就是空语句，写你的代码的时候 要先把pass给删掉。\n",
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "X_train_folds = np.array_split(X_train, num_folds)\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "for k in k_choices:\n",
    "    k_to_accuracies[k] = []\n",
    "    print(\"validate the k is \", k)\n",
    "    # loop the s fold,select the different train and test data\n",
    "    for s in range(num_folds):\n",
    "        X_train_cv = np.vstack(X_train_folds[:s] + X_train_folds[s+1:]) # 将前s份和s+1份到末尾的矩阵链接\n",
    "        y_train_cv = np.hstack(y_train_folds[:s] + y_train_folds[s+1:]) \n",
    "        X_test_cv = X_train_folds[s] # 测试集为第s份数据\n",
    "        y_test_cv = y_train_folds[s] \n",
    "        # train\n",
    "        classifier.train(X_train_cv, y_train_cv)\n",
    "        # compute the distance\n",
    "        dists = classifier.compute_distances_no_loops(X_test_cv)\n",
    "        # predict \n",
    "        y_pred = classifier.predict_labels(dists, k)\n",
    "        # store the test num \n",
    "        num_test_temp = y_test_cv.shape[0]\n",
    "        # compute the accuracy\n",
    "        pred_accuracy = float(np.sum(y_pred == y_test_cv)) / num_test_temp\n",
    "        k_to_accuracies[k].append(pred_accuracy)\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# 输出每次的准确度\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print ('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 如果上述都是正确的话，这里会给出不同的k下交叉验证的准确率的折线图，通过这个图来判断在当前数据集下的最合适的K是多少。\n",
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "  accuracies = k_to_accuracies[k]\n",
    "  plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# 基于上面的交叉验证的结果，来选择一个准确率最高的K，（也就是跟上面的代码输出的图像来判断）\n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 1\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print ('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  }
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